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Best Practices in Talend

APRIL 14, 2023 | BLOGS

Good Job design is at the heart of your Talend development. Here, we look at the different aspects of designing your Jobs. Read about these items in detail by viewing our Talend Best Practice menu items.

Purpose of Job

  • Job Architecture
  • Reusability
  • Error Handling
  • Restart & Recovery
  • Performance (speed of execution)
  • Logging

What is ETL?

ETL stands for Extract, Transform, and Load. It is a process used to integrate data from multiple sources into a target system or database. The ETL process involves extracting data from source systems, transforming the data to meet the target system’s requirements, and then loading the transformed data into the target system.

Here is a brief overview of each step in the ETL process:

Extract: In the extract phase, data is extracted from one or more source systems. The data can be extracted in various formats such as flat files, spreadsheets, or databases.

Transform: In the transform phase, the extracted data is transformed into a format that is compatible with the target system. This can include data cleansing, data filtering, data mapping, and data validation.

Load: In the load phase, the transformed data is loaded into the target system, such as a data warehouse, database, or application.

Where ETL used: The ETL process is a critical component of data integration, and it is used in various industries, including finance, healthcare, retail, and telecommunications. ETL tools, such as Talend, provide a framework for automating the ETL process, making it more efficient and reliable.

What is Talend?

Talend is an ETL tool that provides solutions for data integration, data quality, data preparation, big data and application integration.

There are several reasons why Talend is widely used in the data integration and management industry. Here are some of the key benefits of using Talend:

Open source: Talend is an open-source data integration and management platform, which means that it is available for free and can be customized to meet specific requirements.

Easy to use: Talend provides an easy-to-use graphical user interface (GUI) for designing and managing data integration jobs. It also offers a range of pre-built components that can be used to simplify the integration process.

Wide range of connectors: Talend provides over 1000 connectors that can be used to integrate data from various sources, including databases, cloud applications, and web services.

High performance: Talend uses a multi-threaded architecture to optimize the performance of data integration jobs. It also provides features such as parallel processing and partitioning to further improve performance.

Scalability: Talend is scalable and can be used to manage large volumes of data. It can be deployed on-premise or in the cloud, providing flexibility and scalability.

Comprehensive data management: Talend provides a range of data management features, including data quality, data profiling, and data governance. These features help ensure that data is accurate, complete, and secure.

Overall, Talend is a powerful and flexible data integration and management platform that can help organizations streamline their data integration processes, improve data quality, and gain valuable insights from their data

Best Practices for job design

Job design in Talend refers to the process of creating and configuring data integration jobs using Talend Studio. Here are some best practices for job design in Talend:

Plan your job design: Before you start designing your job, you should have a clear understanding of the data integration requirements and the expected outcomes. Plan the job design and create a flowchart to help you organize the steps in the job.

Use standard components: Talend Studio provides a wide range of components that can be used in job design. It is recommended to use standard components whenever possible, as they have been tested and optimized for performance. Avoid using custom components unless necessary.

Optimize job performance: Talend Studio provides several features to optimize job performance, such as parallel processing and partitioning. Use these features to optimize the performance of your jobs and reduce processing times.

Use metadata: Metadata can be used to store information about the source and destination data, transformations, and mappings. Using metadata can help you create reusable jobs and simplify job maintenance.

Document your job: Documenting your job design can help you understand the job flow and enable others to use or maintain the job in the future. Include a description of each component and explain the job flow using comments.

Test your job: Before deploying your job, test it thoroughly to ensure that it performs as expected. Use test data that represents the real data you will be working with and verify that the job produces the correct output.

Monitor your job: Once your job is deployed, monitor it regularly to ensure that it continues to perform as expected. Use Talend Administration Center to monitor and manage your jobs, and set up alerts to notify you of any issues.

By following these best practices, you can design efficient and effective jobs in Talend that meet your data integration requirements

Best Practice For Data Injection

Data injection in Talend refers to the process of inserting data into a target system, such as a database, data warehouse, or cloud application. Here are some best practices for data injection in Talend:

Plan your data injection: Before you start injecting data, plan the data injection process and create a flowchart to help you organize the steps in the process. Identify the source and target systems, the type of data to be injected, and the expected outcomes.

Validate your data: Validate the data before injecting it into the target system. Ensure that the data is complete, accurate, and consistent with the target system’s requirements. Use Talend’s data profiling and data quality features to validate the data.

Use bulk loading: When injecting large volumes of data, use bulk loading to improve performance. Talend provides several bulk loading components that can be used to optimize data injection, such as tOracleBulkExec and tMSSqlBulkExec.

Use error handling: Implement error handling in your data injection process to handle errors and exceptions. Use Talend’s error handling components, such as tDie and tWarn, to handle errors and notify users of issues.

Monitor your data injection: Once your data injection process is deployed, monitor it regularly to ensure that it performs as expected. Use Talend Administration Center to monitor and manage your jobs, and set up alerts to notify you of any issues.

Document your data injection process: Document your data injection process to help others understand the process and enable them to use or maintain the process in the future. Include a description of each component and explain the process flow using comments.

By following these best practices, you can ensure that your data injection process is efficient, effective, and reliable, and that the injected data is accurate and consistent with the target system’s requirements.

Best Practice for Data Tranformation

Talend is a popular data integration and transformation tool that provides a wide range of features to help you transform data efficiently.Here are some best practices for data transformation in Talend:

Understand the source data: Before you start transforming data, it’s important to understand the source data structure and format. You should know the data types, field lengths, and any data quality issues.

Use Talend’s built-in components: Talend provides a wide range of built-in components that can help you transform data efficiently. You should leverage these components as much as possible instead of writing custom code.

Use best practices for data quality: Data quality is crucial for effective data transformation. You should implement best practices for data quality, such as data profiling, data cleansing, and data validation.

Use metadata management: Metadata management helps you understand the data lineage and relationships between different data elements. You should use metadata management tools to maintain an accurate and up-to-date view of your data.

Implement error handling: Data transformation can sometimes lead to errors, such as data type mismatches or null values. You should implement error handling to catch and handle these errors gracefully.

Use version control: Talend provides version control for your data integration jobs. You should use version control to track changes to your jobs and maintain a history of your data transformations.

Optimize performance: Data transformation can be resource-intensive, especially when dealing with large volumes of data. You should optimize performance by using parallel processing, caching, and other optimization techniques.

By following these best practices, you can ensure that your data transformations in Talend are efficient, accurate, and maintainable

Let us handle the heavy lifting and ensure your data is safe and secure throughout the process.

Best Practice for Data loading

Use the appropriate data integration components: Talend offers a wide range of data integration components that are designed for different data sources and targets. Use the appropriate components for the data you are working with to ensure optimal performance and accuracy.

Optimize data mapping: Map your data sources and targets in the most efficient way possible. Talend allows you to use several mapping methods, such as direct mapping, lookup, join, and filter. Choose the appropriate method for your data integration needs.

Implement error handling: Ensure that your data integration jobs have appropriate error handling mechanisms in place. This will help you detect and correct errors during the loading process.

Monitor performance: Monitor the performance of your data integration jobs regularly. This will help you identify bottlenecks and optimize the loading process for faster and more efficient data loading.

Use parallel processing: Talend allows you to perform parallel processing of data. Use this feature to improve performance and speed up the data loading process.

Validate data: Validate your data before and after loading to ensure accuracy and completeness. Talend provides data validation components that can help you achieve this.

Document your jobs: Document your data integration jobs thoroughly. This will help you and other users understand the job flow and make it easier to maintain and modify the jobs in the future.

Overall, these best practices will help you optimize data loading in Talend and achieve better data integration outcome

Best Practice for Error Handling

Error handling is an important aspect of any data integration process, and Talend provides several features and best practices to handle errors effectively. Here are some of the best practices in error handling in Talend:

Use Try/Catch blocks: Talend provides Try/Catch blocks to handle exceptions. Use Try/Catch blocks in your Talend jobs to capture and handle errors.

Use OnComponentError: The OnComponentError trigger allows you to define a custom error handling routine for each component in your job. Use this trigger to handle errors for specific components.

Use tDie and tWarn: Use tDie and tWarn components to terminate the job and log a message when an error occurs.

Use tLogCatcher: Use tLogCatcher component to capture log messages and error messages generated by Talend components.

Use tFlowToIterate: Use tFlowToIterate component to iterate over the data in case of error in Talend job.

Use tJavaRow: Use tJavaRow component to add custom Java code to handle errors.

Use tJavaCatch: Use tJavaCatch component to handle Java exceptions.

Use tAssertCatcher: Use tAssertCatcher component to handle errors in Assert routines.

Use tAssert and tAssertEquals: Use tAssert and tAssertEquals components to perform assertions on your data and check for errors.

Use tLogRow: Use tLogRow component to log data to the console or a file to help diagnose errors.

By following these best practices in error handling in Talend, you can ensure that your data integration processes are more robust and reliable.

Best practices Of Null Pointer exception

Null pointer exceptions can occur in Talend when a component attempts to access an object or variable that is null. Here are some best practices to avoid or handle null pointer exceptions in Talend:

Check for null values: Always check for null values before accessing any object or variable. You can use the tJavaRow component to check for null values and perform any required actions.

Use the “if” condition: Use the “if” condition to check for null values in your Talend job. This will help you to avoid null pointer exceptions.

Use the “isNull” function: Use the “isNull” function in Talend to check whether an object or variable is null. This will help you to avoid null pointer exceptions.

Use the “try-catch” block: Use the “try-catch” block to catch any null pointer exceptions that may occur in your Talend job. This will help you to handle the exceptions and avoid job failures.

Use the “tFilterRow” component: Use the “tFilterRow” component in Talend to filter out any null values from your data. This will help you to avoid null pointer exceptions and ensure that only valid data is processed.

By following these best practices, you can avoid or handle null pointer exceptions in your Talend job and ensure that it runs smoothly and efficiently.

Conclusion

In conclusion, ETL is a crucial process that integrates data from multiple sources into a target system. With Talend, a popular ETL tool, organizations can benefit from its open-sourcenature, ease-of-use, and scalability. To ensure successful job design, it’s important to plan, use standard components, optimize job performance, use metadata, document the job, test it thoroughly, and monitor it regularly. Similarly, when injecting data, it’s essential to plan the process, validate the data, use bulk loading for large volumes, and implement error handling. By implementing these best practices, organizations can achieve efficient and effective ETL jobs and injection processes that meet their data integration needs.

Have a Question?

Speak to an Expert

Puneet Taneja
CPO (Chief Planning Officer)

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The Ultimate Guide to Solving Data Challenges in Organizations

FABRUARY 10, 2023 | BLOGS

Data has become one of the most critical components of modern organizations. In the digital age, companies rely on data to make informed decisions, optimize operations, and drive growth. However, despite its importance, data can also pose significant challenges that organizations must tackle to unlock its full potential.

In this blog post, we will cover the most common data challenges faced by organizations and provide actionable strategies for addressing them.

Data Quality Challenge

Data quality refers to the accuracy, completeness, and reliability of data. In organizations, data quality is critical for decision-making, and poor data quality can lead to incorrect decisions and harm the organization’s reputation. To tackle the data quality challenge, organizations must implement a data governance framework, create a data quality plan, and perform regular data cleansing.

Data Governance Framework
A data governance framework outlines the policies, procedures, and standards for managing data within an organization. It ensures that data is managed consistently and effectively across all departments.

Data Quality Plan
A data quality plan outlines the steps required to improve data quality, including data cleansing and data validation. It should also define the roles and responsibilities of individuals and teams within the organization responsible for data quality.

Data Cleansing
Data cleansing involves identifying and correcting inaccuracies in data, such as duplicate records or incorrect information. Regular data cleansing helps ensure data quality and reliability.

By addressing the data quality challenge, organizations can improve the accuracy of their decision-making and protect their reputation.

Data Security Challenge

Data security refers to the measures organizations take to protect sensitive data from unauthorized access or theft. With the increasing number of data breaches, data security is more critical than ever. To tackle the data security challenge, organizations should implement encryption, firewalls, access controls, and employee education programs.

Encryption
Encryption helps protect sensitive data by converting it into a code that can only be decrypted by authorized individuals. Encryption is a critical component of data security.

Firewalls
Firewalls act as a barrier between the organization’s network and the internet, protecting sensitive data from unauthorized access.

Access Controls
Access controls determine who has access to sensitive data within an organization. Organizations should limit access to only those individuals who require it to perform their job functions.

Employee Education
Employee education programs help ensure that employees understand the importance of data security and the measures they can take to protect sensitive data.

By addressing the data security challenge, organizations can protect sensitive data from theft and minimize the risk of data breaches.

Data Integration Challenge

Data integration refers to the process of combining data from different sources into a single view. In organizations, data integration is critical for creating a holistic view of operations and making informed decisions. To tackle the data integration challenge, organizations should use data integration tools, perform data mapping, and implement data warehousing.

Data Integration Tools
Data integration tools automate the process of combining data from different sources into a single view. They are essential for organizations seeking to improve data integration. 

Data Mapping
Data mapping involves creating a visual representation of how data is stored in different systems and how it should be combined. It helps ensure that data is correctly integrated and that all stakeholders understand how data is being

integration challenge, organizations can create a holistic view of their operations and make informed decisions based on accurate and complete data.

Data Analysis Challenge

Data analysis refers to the process of making sense of data and turning it into actionable insights. In organizations, data analysis is critical for decision-making and driving growth. To tackle the data analysis challenge, organizations should use data analytics tools, hire data scientists, and implement predictive analytics.

Data Analytics Tools
Data analytics tools provide organizations with the ability to analyze large amounts of data and uncover insights. These tools can help organizations make informed decisions and drive growth.

Data Scientists
Data scientists are individuals who specialize in using data to solve complex problems. They are essential for organizations seeking to maximize the value of their data.

Predictive analytics
Predictive analytics involves using historical data to make predictions about future trends and patterns. It helps organizations make proactive decisions and prepare for potential risks.

By addressing the data analysis challenge, organizations can unlock the full potential of their data and drive growth through informed decision-making.

Benefits of Solving Data-Challenges in your business

Data is a valuable asset in any business, and managing it effectively can provide numerous benefits. Solving data-related challenges is a critical step in leveraging the power of data to drive business success. Here are some of the benefits of solving data-challenges in your business:

  • Improved Decision-Making: Data provides valuable insights into business operations and can inform decision-making processes. By solving data-challenges, organizations can ensure that their data is accurate, reliable, and up-to-date, providing a solid foundation for informed decision-making.
  • Increased Productivity: Data-challenges can cause delays and inefficiencies, reducing productivity and profitability. Solving these challenges can improve productivity by streamlining data-related processes and reducing the time and resources spent on manual data management tasks.
  • Better Customer Experience: Data provides valuable insights into customer behavior and preferences, allowing organizations to better understand and serve their customers. By solving data-challenges, organizations can ensure that they have accurate and up-to-date information about their customers, which can help to improve the customer experience.
  • Increased Competitiveness: Data-driven insights can help organizations gain a competitive edge by allowing them to make better decisions and respond more effectively to market trends and changes. Solving data-challenges can ensure that organizations have access to the data they need to stay ahead of their competitors.
  • Improved Data Governance: Data governance is critical for ensuring data quality and security. By solving data-challenges, organizations can establish effective data governance practices that ensure data quality, security, and compliance.

Conclusion

In conclusion, organizations face several data challenges that must be addressed to unlock the full potential of data. By addressing data quality, security, integration, and analysis challenges, organizations can improve the accuracy of their decision-making, protect sensitive data, create a holistic view of operations, and drive growth. This guide provides actionable strategies for addressing the most common data challenges faced by organizations. By implementing these strategies, organizations can overcome these challenges and maximize the value of their data.

Let us handle the heavy lifting and ensure your data is safe and secure throughout the process.

Speak to an Expert

Complere can help

Complere provides a comprehensive solution for solving data challenges by combining advanced technologies with expert personnel. Our data validation services help businesses streamline data validation processes, ensure accurate and up-to-date information, and improve data governance. 

We also offer consultation and implementation services for data management and data security best practices. Contact Complere to solve your data challenges and improve your data management for better business results.

Take the first step towards better data challenges. Contact Complere today by calling 7042675588. Our team will be happy to discuss our services and how we can help your business.

Have a Question?

Puneet Taneja
CPO (Chief Planning Officer)

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Data Warehouses: Empowering Business Owners with Better Data Insights

FABRUARY 09, 2023 | BLOGS

In the modern world of technology, data has become an essential part of every business. The success of a business depends on how efficiently it uses the data available to it. A data warehouse is a system that is designed to store and manage vast amounts of data for analysis, reporting, and decision-making.

In this article, we will dive into the world of data warehousing and explore the various concepts related to it.

What is a Data Warehouse?

A data warehouse is a centralized repository of data that is designed to store data from multiple sources. The data stored in a data warehouse is used to support business intelligence activities, such as reporting, data analysis, and data mining. A data warehouse is optimized for querying and analysis, which makes it an ideal platform for data-driven decision-making.

Basic Concepts of a Data Warehouse

Understanding the basic concepts of a data warehouse is critical to effectively using one to drive business decisions and improve organizational performance. Here are some of the basic concepts of a data warehouse:

Data Integration
Data warehouses allow organizations to integrate data from various sources, such as ERP systems, CRM systems, and databases, into a single repository. This makes it easier for organizations to access the data they need to make informed decisions.

Data Modeling
Data modeling is the process of defining the structure of the data in a data warehouse. It involves creating a schema that defines how the data will be stored, accessed, and analyzed.

Facts and Diemensions
Facts and dimensions are the two essential components of a data warehouse. Facts are numerical data that can be measured, while dimensions are descriptive data that provide context to the facts.

OLAP
OLAP stands for Online Analytical Processing. It is a type of software that allows users to analyze large amounts of data from a data warehouse.

Data Warehousing Solutions
There are several data warehousing solutions available in the market, such as Oracle, Microsoft SQL Server, and IBM. Organizations must choose a data warehousing solution that fits their needs and budget.

Data Cleansing
Data warehouses allow organizations to clean and standardize data, which leads to increased accuracy and improved analysis.

Data Warehousing Architecture
The architecture of a data warehouse is critical to its success. It must be designed to handle large amounts of data, provide quick access to data, and ensure data security.

Fact and Dimensions: The Key Components of Data Warehousing

Data warehousing is a critical component of business intelligence, and understanding the concepts of facts and dimensions is essential to effectively using a data warehouse to drive better business decisions. A fact is a numerical data point that can be measured and analyzed, while a dimension is a descriptive data point that provides context to the facts. Together, facts and dimensions form the basis of a data warehouse and are used to analyze large amounts of data to inform business decisions.

Now question often arises how we should identify Facts and Diemensions?

We can identify Facts and Diemensions by following these steps:

Understanding Business Requirements
The first step in identifying facts and dimensions is to understand the business requirements. What kind of data do you need to analyze to make informed business decisions? This can help you determine the type of facts and dimensions you need to store in your data warehouse.

Identifying Measurement Metrics
Facts are numerical data points that can be measured. To identify the facts in your data warehouse, you need to identify the measurement metrics that are critical to your business. This may include sales, customer count, or product count, among others.

Understanding Contextual Data
Dimensions provide context to the facts. To identify the dimensions in your data warehouse, you need to understand the contextual data that is relevant to your business. This may include customer data, product data, or location data, among others.

Examining Relationships among Data
Finally, you need to examine the relationships between the data points in your data warehouse. This can help you identify which data points are facts and which are dimensions.

So, facts and dimensions are the key components of a data warehouse, and understanding how to identify them is essential to effectively using a data warehouse to drive better business decisions. By following these steps, you can ensure that your data warehouse contains the right data to inform your business decisions and improve your organizational performance.

Why Should We Use a Data Warehouse in Our Business?

There are many reasons why a business should use a data warehouse. Firstly, a data warehouse provides a centralized repository of data, which makes it easier for the business to manage and analyze data from multiple sources. Secondly, a data warehouse is designed for querying and analysis, which makes it an ideal platform for data-driven decision-making. Finally, a data warehouse can help a business to reduce data redundancy and improve data accuracy, which leads to better decision-making and improved business performance.

How to Implement a Data Warehouse for a Business: A Step-by-Step Guide

Implementing a data warehouse is a critical step for organizations that want to improve their business intelligence and make informed decisions. A data warehouse provides a centralized repository of business-critical data that can be analyzed to drive better business outcomes. Here is a step-by-step guide on how to implement a data warehouse for your business.

Requirement Analysis and Capacity Planning
This is the first and most important step in the data warehouse implementation process. It involves analyzing the business requirements, determining the goals and objectives of the data warehouse, and planning the capacity and scalability of the system. This step is crucial in ensuring that the data warehouse meets the needs of the business and can handle the expected volume of data.

Hardware Integration
In this step, the hardware components of the data warehouse are integrated and configured to work together seamlessly. This includes the selection of servers, storage devices, and networking components that will support the data warehouse.

Data Modelling
This step involves creating a conceptual and logical model of the data that will be stored in the data warehouse. This model serves as a blueprint for the physical design of the data warehouse and helps to ensure that the data is organized and structured in a way that meets the business requirements.

Physical Modelling
In this step, the conceptual and logical data model is transformed into a physical data model that can be implemented in a database. The physical model includes the design of tables, relationships between tables, and other database objects.

Data Sources
The next step is to identify and extract data from various sources such as transactional systems, operational databases, or other data sources. The data is then transformed and loaded into the data warehouse.

ETL
The data extracted from various sources must be transformed into a format that can be stored in the data warehouse. This process is called Extract, Transform, Load (ETL) and involves cleansing, transforming, and loading the data into the data warehouse.

Populate the Data Warehouse
In this step, the transformed data is loaded into the data warehouse, and the data is populated with real data. This step is crucial in ensuring that the data in the data warehouse is accurate and up-to-date.

User Application
In this step, user applications are developed to interact with the data warehouse. These applications can include reporting, analysis, and data visualization tools that allow users to access and analyze the data stored in the data warehouse.

Roll-Out the Warehouse and Applications
This is the final step in the data warehouse implementation process, and involves rolling out the data warehouse and applications to the users. This includes training users on how to use the applications and providing support and maintenance for the data warehouse and applications.

In conclusion, implementing a data warehouse is a complex process that requires careful planning and execution. By following the steps outlined in this blog post, you can ensure that your data warehouse is implemented successfully and meets the needs of your business.

Advantages of Using a Data Warehouse in Our Business

A data warehouse is a centralized repository of business-critical data that provides organizations with the ability to analyze large amounts of data and make informed decisions. The use of data warehouses has increased in recent years, and there are several key advantages to using a data warehouse in our business.

Improved Data Accuracy
A data warehouse provides a centralized location for storing business data, which helps to improve the accuracy and consistency of data. This is critical for organizations that want to make informed decisions based on accurate data.

Faster Data Access
Data warehouses allow for faster access to business-critical data, as data is stored in a centralized location and optimized for fast retrieval. This is important for organizations that need to quickly access data to make informed decisions.

Better Data Analysis
Data warehouses provide powerful analytics tools that allow organizations to analyze large amounts of data to uncover insights and drive better business outcomes. This is essential for organizations that want to make informed decisions based on data.

Improved Data Security
Data warehouses provide a centralized location for storing business data, which makes it easier to implement security measures to protect sensitive data. This is important for organizations that need to ensure the security and confidentiality of their business data.

Cost Saving
By consolidating data into a single repository, organizations can reduce the cost of storing and managing data. This is important for organizations that are looking to reduce costs while improving their data management capabilities.

In conclusion, there are several key advantages to using a data warehouse in our business. From improved data accuracy and faster data access to better data analysis and improved data security, a data warehouse can help organizations to make informed decisions and drive better business outcomes.

As a Business Owner, Why We Need to Develop a Data Warehouse

As a business owner, having access to accurate and valuable business data is essential for making informed decisions and driving better business outcomes. One of the most effective ways to manage and maximize the value of your business data is by developing a data warehouse. A data warehouse is a centralized repository for storing and managing large amounts of business data, which makes it easier to manage, analyze, and secure this data.

One of the key benefits of having a data warehouse is improved data management. A data warehouse makes it easier to manage large amounts of data and ensures that the data is accurate, consistent, and easily accessible. Additionally, data warehouses provide powerful analytics tools that allow organizations to uncover insights and drive better business outcomes.

This is critical for organizations that want to make informed decisions based on data. Furthermore, data warehouses provide a centralized location for storing business data, making it easier to implement security measures to protect sensitive data. This is important for organizations that need to ensure the security and confidentiality of their business data.

Developing a data warehouse can also result in cost savings by consolidating data into a single repository and reducing the cost of storing and managing data. Additionally, having a centralized repository of business data can provide organizations with a competitive advantage, as they can make informed decisions faster and more effectively than those without a data warehouse.

Choosing the Right Data Warehousing Solution

A data warehouse is a crucial tool for organizations that want to maximize the value of their business data. With the right data warehousing solution, organizations can consolidate, manage, and analyze large amounts of data to drive better business outcomes. However, with so many data warehousing solutions on the market, it can be difficult to know which one is right for your organization. In this article, we’ll explore some key factors to consider when choosing the right data warehousing solution.

 

Data Volume and Scale
Consider the amount of data that your organization generates and the amount of data that you need to store. This will help you determine the size and scale of the data warehousing solution you need.

Analytics Requirements
Consider the type of analytics that your organization needs to perform. This will help you determine the capabilities of the data warehousing solution that you need.

Integration with Other Systems
Consider the other systems that your organization uses, such as CRM, ERP, and BI. Make sure that the data warehousing solution you choose can integrate with these systems to ensure that you get the most value from your data.

Cost
Consider the cost of the data warehousing solution and how it fits within your budget. You want to ensure that you get the most value for your money while still meeting your data warehousing requirements.

Support and Maintenance
Consider the level of support and maintenance that you need for the data warehousing solution. Make sure that the solution you choose has a strong support and maintenance plan to ensure that your data is always available and secure.

So, choosing the right data warehousing solution is critical for organizations that want to maximize the value of their business data. By considering factors such as data volume and scale, analytics requirements, integration with other systems, cost, and support and maintenance, organizations can make informed decisions about which data warehousing solution is right for their needs.

Benefits of Having data warehouse in your business

A data warehouse is a centralized repository of data that allows organizations to store, manage, and analyze large amounts of structured and unstructured data. Having a data warehouse in your business can bring many benefits, including:

  • Improved Business Intelligence: Data warehouses provide a single source of truth for all data, allowing organizations to easily access and analyze data from various sources. This can improve business intelligence by providing a comprehensive view of all data, allowing organizations to make informed decisions and respond more effectively to market trends and changes.
  • Increased Efficiency: Data warehouses eliminate the need for manual data processing and can improve the efficiency of data analysis processes. This can free up time and resources, allowing organizations to focus on more strategic tasks.
  • Better Data Management: Data warehouses provide a centralized and secure platform for data management, ensuring that data is accurate, reliable, and in compliance with industry standards. This can improve data governance and enhance data security.
  • Enhanced Customer Insights: Data warehouses provide valuable insights into customer behavior and preferences, allowing organizations to better understand and serve their customers. This can help organizations to improve the customer experience and increase customer satisfaction.
  • Competitive Advantage: Data-driven insights can help organizations gain a competitive edge by allowing them to make better decisions and respond more effectively to market trends and changes. Data warehouses provide a centralized repository for all data, making it easier for organizations to access the data they need to stay ahead of their competitors.

Conclusion

In conclusion, data warehouses play a crucial role in maximizing the value of business data by consolidating, managing, and analyzing it. Understanding the basic concepts, such as facts and dimensions, and implementing a solution that fits your business’ needs can lead to improved data management, enhanced analytics, better security, and cost savings. Choosing the right data warehousing solution should consider factors such as data volume, analytics requirements, system integration, cost, and support. A well-designed data warehouse can unlock the secrets to better business analytics and drive better outcomes.

Let us handle the heavy lifting and ensure your data is safe and secure throughout the process.

Speak to an Expert

Complere can help

Complere is a technology that empowers business owners with better data insights. It integrates data from various sources to provide a comprehensive view of business operations and financials. Complere eliminates manual data entry and minimizes errors, allowing business owners to focus on analyzing the data and making informed decisions.

It provides real-time insights into financial performance, customer behavior and business performance, helping business owners improve customer satisfaction and make better financial decisions. Complere is an essential tool for business owners looking to improve their business and reach their goals.

Call the Complere team at 7042675588 today to learn more about our data services and how we can help you.

 

Have a Question?

Puneet Taneja
CPO (Chief Planning Officer)

Table of Contents

Complere can help

Case study on Flolite
casestudy

Case study on Flolite

November 16, 2022 | casestudy |
Improve the productivity & build high performance Challenge Leading a team, whether it consists of ten or a hundred people, is incredibly challenging. Analyzing the team’s performance...
Read More
How automated reports helps in increasing the productivity.
casestudy

How automated reports helps in increasing the productivity.

November 9, 2022 | casestudy |
How automated reports helps in increasing the productivity? Challenge Automated reports systems help businesses track the support team’s performance, goals, & self-evaluation to increase...
Read More
meeting
casestudy

Case Study: Textellent

July 18, 2022 | casestudy |
Textellent has to build at speed and scale to handle clients' info uploads of Textellent customers from various Tax software, before the import files required to be validated and then processed...
Read More
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Mastering the art of software testing

FEBRUARY 09, 2023 | BLOGS

Software testing is the process of evaluating a software application to identify any defects, bugs or errors and to ensure its functionality and performance meets the specified requirements. Testing is a crucial step in the software development life cycle and helps to ensure the quality of the final product delivered to the customer.

It involves executing a series of tests on the software to verify its behavior, performance, security and compatibility. Effective software testing can uncover hidden issues early in the development process, reducing the risk of costly rework and increasing the likelihood of delivering a successful product.

What is software testing?

Software testing is the process of evaluating software to determine if it meets the specified requirements and functions as expected. The purpose of software testing is to identify defects and ensure software quality before it is released to the market. Software testers play a crucial role in the development process by evaluating the software and providing feedback to developers.

The goal of software testing is to ensure that the software is functioning as intended, providing a high level of quality and reliability to the end-user.

Why do we test software?

Testing software is a crucial step in the software development process. It ensures that the software meets the specified requirements and functions as intended. Here are a few reasons why testing is important:

  • To identify bugs and fix them
  • To ensure software quality
  • To ensure compatibility
  • To meet customer expectations
  • To catch issues early

Software Testing: Where it Can Be Applied

Software testing is a vital part of software development that ensures the quality and reliability of a software product. In this blog post, we will discuss various domains and industries where software testing can be applied.

Web Applications
In web applications, software testing helps to ensure that the website is functioning correctly and meets the needs of the users. It verifies that the website is accessible, secure, and performs well under different load conditions.

Mobile Applications
Mobile industries use software testing to verify the functionality and performance of mobile applications. Testing is essential to ensure that the application is compatible with different mobile devices and operating systems and delivers a high-quality user experience.

Gaming Industeries
Gaming industries also rely on software testing to ensure that the game is functioning correctly and provides a high-quality gaming experience. Testing is used to verify the functionality and performance of the game, as well as its compatibility with different platforms and devices.

Healthcare Industeries
Healthcare industries use software testing to verify the accuracy and reliability of medical software, such as electronic health record systems and medical imaging software. This is critical for ensuring patient safety and the accuracy of medical diagnoses and treatments.

Automation and Control System
In automation and control systems, software testing is used to ensure that the system is functioning correctly and providing the intended results. This is critical for ensuring the safe and efficient operation of systems in industries such as manufacturing, transportation, and energy.

Financial Syatems
Financial systems also rely on software testing to ensure the accuracy and reliability of financial transactions and the protection of sensitive financial data. This is critical for ensuring the security and stability of the financial system.

Education Technology
In education technology, software testing is used to ensure that educational software, such as learning management systems and e-learning platforms, is functioning correctly and providing an effective learning experience for students.

Verification vs Validation in Software Testing

Verification and validation are two important concepts in software testing.

Verification refers to the process of evaluating the software during its development to determine if it satisfies specified requirements. It involves checking that the software design, plans, and requirements are complete, consistent, and accurate. This typically involves reviewing documents, conducting inspections, and performing simulations.

Validation, on the other hand, refers to the process of evaluating the software after it has been completed to determine if it meets the specified requirements. It involves testing the software to see if it functions as intended and if it meets the needs of the end-user. Validation is focused on evaluating the software’s performance, reliability, and user satisfaction.

Verification ensures that the software is built right, while validation ensures that it is built right for the right purpose.

Functional Requirements vs Non-Functional Requirements

Functional and non-functional requirements are two important aspects of software testing. Functional requirements describe the specific actions the software should perform and the functionality it should provide. Examples include the ability to log in, add items to a cart, or send an email. On the other hand, non-functional requirements describe the qualities of the software such as performance, reliability, and usability. Examples include response time, security, and accessibility.

Both functional and non-functional requirements play a crucial role in ensuring the quality and reliability of the software. Testing functional requirements verifies that the software meets the end-users’ needs, while testing non-functional requirements validates the performance, reliability, and user experience of the software. Both types of requirements must be considered and tested to ensure that the software is of high quality and meets the expectations of the end-users.

Types of Software Testing: A Quick Overview.

Software testing plays a crucial role in software development by evaluating the performance and functionality of an application. There are various types of testing each with a unique purpose and method.

Unit Testing
Unit testing is a type of testing that focuses on individual components or modules of a software application. It is usually performed by developers themselves and aims to validate the correctness of each unit. Unit testing is typically done at an early stage of the software development process, and it helps to catch errors before they become more complicated and expensive to fix.

Integration Testing 
Integration testing is the next step after unit testing. In this type of testing, multiple components of the software system are tested to see if they are working together as expected. Integration testing helps to identify and resolve any compatibility issues between components, ensuring that the software system operates smoothly as a whole.

System Testing
System testing is the final step before software release. In this type of testing, the entire software system, including all its components, is tested to verify that it meets the specified requirements. System testing helps to identify any issues that may have been missed in previous testing stages and ensures that the software is ready for release.

Regression Testing
Regression testing is a type of testing that is performed after changes have been made to the software system. The purpose of regression testing is to verify that the changes have not caused unintended consequences and that the existing functionality is still working correctly. This type of testing helps to maintain the quality of the software and catch any errors that may have been introduced during the modification process.

Acceptance Testing
Acceptance testing is the final stage of software testing and is performed by end-users or customers. The purpose of acceptance testing is to determine if the software is suitable for their needs and meets their expectations. Acceptance testing helps to ensure that the software is ready for release and that it will be well-received by its intended audience.

What is ETL Testing?

ETL testing is the process of verifying the accuracy and efficiency of the data transfer process from multiple sources to a data warehouse. It involves the steps of Extract, Transform, and Load. The key components of ETL testing include:

  • Data validation
  • Data mapping
  • Data integrity
  • Performance testing

ETL testing ensures that the data in the data warehouse is accurate and reliable, allowing organizations to base their analysis and reporting on trusted data. It is a crucial component of data warehousing and business intelligence.

Black Box vs White Box Testing

Black Box Testing is a testing technique that focuses on the functionality of the software without any consideration for the internal workings or code structure. The tester is only concerned with the input and output, and has no knowledge of the internal implementation. This type of testing is also known as functional testing and is focused on verifying that the software meets the specified requirements.

White Box Testing, also known as structural testing, is a testing technique that focuses on the internal structure of the software, including the code and design. The tester has knowledge of the code and tests the software based on its internal workings and structure, as well as its functionality. This type of testing is focused on finding errors and defects in the code, design, and logic.

Black Box Testing evaluates the software from the outside, while White Box Testing evaluates the software from the inside. Both approaches are important for ensuring the quality and reliability of the software.

Defect Life Cycle

 The Bug Life Cycle, also known as the Defect Life Cycle, is a process that software testing teams follow to track and manage software bugs from the moment they are reported to the moment they are fixed and closed. The process typically includes the following stages:

Bug Reporting
A bug is reported by a tester, developer, or user. This stage involves documenting the issue and its symptoms, as well as providing information on the environment in which the issue was encountered.

Bug Triage
The reported bug is triaged, meaning it is prioritized and assigned to the appropriate team member for further investigation. This step is crucial, as it determines the order in which bugs are fixed, with the most critical issues addressed first.

Bug Analysis
The assigned team member investigates the issue and determines the cause of the bug. This stage is essential to determining the right course of action to resolve the issue.

Bug Fixing
A solution to the bug is created and tested. This stage may involve writing new code, modifying existing code, or updating software configurations.

Bug Verification 
The solution is tested to ensure that it resolves the issue. If the solution does not resolve the issue, the Bug Life Cycle starts again from the bug analysis stage.

Bug Clousre 
If the solution does resolve the issue, the bug is marked as closed. The closure process may involve updating bug tracking systems, communicating the resolution to stakeholders, and documenting the fix.

The Bug Life Cycle is a critical process for software testing teams, as it provides a structured approach to managing bugs and ensuring that they are fixed in a timely and effective manner. By following this process, teams can improve the quality of their software and provide a better experience for their users.

In addition to the stages mentioned above, the Bug Life Cycle may also include additional steps, such as regression testing, which involves re-testing the software after the bug has been fixed to ensure that the fix has not introduced new issues.

 

Bug Severity and Priority

Bug Severity and Priority are two important factors in software testing that determine the impact of a defect on the software and the urgency with which it should be fixed.

Bug Severity is a measure of the impact of a defect on the software and how it affects the software’s functionality. Severity is usually classified as low, medium, or high, depending on the extent to which the defect affects the software. For example, a low-severity defect may cause a minor cosmetic issue, while a high-severity defect may prevent the software from functioning properly.

Bug Priority is a measure of the urgency with which a defect should be fixed, based on its impact on the software and the users. Priority is usually classified as low, medium, or high, and is used to prioritize the fixing of defects based on their impact and urgency. For example, a high-priority defect may be causing significant disruption to the software’s functionality and needs to be fixed immediately, while a low-priority defect may have a minor impact and can be fixed at a later time.

Bug Severity determines the impact of a defect on the software, while Bug Priority determines the urgency with which it should be fixed. Both factors are important for ensuring the quality and reliability of the software.

Test Deliverables

Test deliverables refer to the outputs of a software testing process. They are the results of the testing activities and are used to measure the quality of the software being developed. Test deliverables are an important aspect of software development as they help stakeholders understand the state of the software and identify any issues that need to be addressed.

There are several common types of test deliverables, including test plans, test cases, test scripts, test data, and test results.

Test Plan
A test plan is a document that outlines the testing approach for a software project. It includes the scope of testing, testing objectives, test environment, test schedule, test cases, and resource requirements.

Test Cases
Test cases are a set of conditions or variables under which a tester will determine whether the software works as intended. They are used to validate the functionality and performance of the software and help identify any defects or issues.

Test Scripts
Test scripts are step-by-step instructions for executing a test case. They provide detailed instructions on how to perform the testing, what inputs to use, and what the expected results are.

Test Data
Test data refers to the data used in testing the software. It can include input data, expected results, and actual results.

Test Results
Test results are the outcomes of the testing process. They include information on whether the software passed or failed the test, any issues identified, and the status of any defects.

Test Deliverables are an essential aspect of software development and play a crucial role in ensuring the quality of the software. They provide valuable information to stakeholders and help identify any issues that need to be addressed before the software is released to the market.

Benefits of Software testing in your Business

We know that inaccurate data costs the business time, money, and resources. Therefore, having high-quality data is essential for accuracy and dependability. The benefits of data validation in your business are listed below: 

  • Improved Quality: Testing helps identify and resolve defects and errors in software, resulting in higher quality and more reliable systems.
  • Increased User Satisfaction: With software systems that are free of bugs and errors, users are more likely to be satisfied with the overall performance of the software.
  • Enhanced Security: Testing helps identify security vulnerabilities and resolve them, thereby reducing the risk of security breaches and data theft.
  • Better User Experience: Testing helps ensure that software systems are user-friendly, easy to navigate and perform as expected, leading to a better user experience.
  • Cost Savings: By catching defects and errors early in the development process, testing can reduce the overall cost of development and maintenance.
  • Faster Time to Market: By ensuring that software systems are reliable and functional, testing helps reduce the time to market, allowing businesses to bring new products and services to market faster.
  • Improved Business Reputation: Reliable and functional software systems can enhance the reputation of a business, increasing customer confidence and trust.

Let us handle the heavy lifting and ensure your data is safe and secure throughout the process.

Complere can help

Complere is a technology that helps businesses improve their software testing process. It integrates data from various sources to provide real-time insights into software performance, customer behavior, and financial performance. 

Complere automates testing processes, freeing up testers to focus on complex issues, and provides financial insights to inform software development decisions. With Complere, businesses can achieve improved software quality and reliability, leading to a better user experience.

Call the Complere team at 7042675588 today to learn more about our Software testing services and how we can help you.

Have a Question?

Puneet Taneja
CPO (Chief Planning Officer)

Table of Contents

Complere can help

Case study on Flolite
casestudy

Case study on Flolite

November 16, 2022 | casestudy |
Improve the productivity & build high performance Challenge Leading a team, whether it consists of ten or a hundred people, is incredibly challenging. Analyzing the team’s performance...
Read More
How automated reports helps in increasing the productivity.
casestudy

How automated reports helps in increasing the productivity.

November 9, 2022 | casestudy |
How automated reports helps in increasing the productivity? Challenge Automated reports systems help businesses track the support team’s performance, goals, & self-evaluation to increase...
Read More
meeting
casestudy

Case Study: Textellent

July 18, 2022 | casestudy |
Textellent has to build at speed and scale to handle clients' info uploads of Textellent customers from various Tax software, before the import files required to be validated and then processed...
Read More

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Improve the productivity & build high performance

Challenge

Leading a team, whether it consists of ten or a hundred people, is incredibly challenging. Analyzing the team's performance isn't our cup of tea either. Our management was having a lot of trouble keeping track of the employees' performance while managing the team. 

Employees were not motivated & competitive. They were not exhibiting much involvement in their work, and there were many more challenges we were encountering since they didn't know where they needed to improve, their roles were unclear, and they could not spot the challenging areas. To run a successful company, we needed to be on the same page, which was difficult. 

What does Complere do?

 

We made Flolite a part of our company culture. To begin, we gave staff a template for a customized SAAS-based application for maintenance, which requires managers/leaders to have. Then, monthly one-on-ones using a pre-defined template aided them in providing feedback and determining the next steps for improvement. As a result, we saw significant productivity and performance improvements from using Flolite. 

The Flolite application mandates quarterly assessment, during which the team will be able to enhance their complex area with the assistance of mentors.  

  • This application enables our employees for rewards & recognition, which makes them motivated & competitive. 
  • Implementation of Flolite, we build the Culture of our Dreams 
  • Flolite provides companies with the tools to offer perks that employee Management 

The Scope of the engagement included the following initiative.

 

  • Conduct a quarterly performance review and keep track of it 
  • Build trust by revisiting goals, role clarity & ensuring growth 
  • Drive motivation by appreciating our team members 
  • Custom reports assessing the competency and engagement of our employees 
  • To assure action completion, we develop a procedure for reminding services 

Complere Solution benefited in many ways:

  • Our workplace is now a happier one 
  • Employees that are highly motivated and skilled 
  • Employees can recognize the problematic areas 
  • We are raising Flolite awareness and improving cultural literacy among our employees. 
  • Within six months of using Flolite, the company’s culture had improved from 50% to 80%, as everyone knew where they stood and where they needed to improve 
  • Employees are developing hidden talent with experienced mentors  

Summary

 

With highly personalized monthly 1-on-1, peer feedback, and quarterly reviews, it is easier for the team to fulfill the progress goals, resulting in high productivity. Flolite gives businesses the tools they need to offer employee perks while sustaining company culture. This statistic aids management in tracking team member performance and increasing team productivity. With these metrics, our employees have increased productivity and built excellent performance. As a result, they are more satisfied and happy in their current position. 

Social media post promoting this case study

 

Improve productivity & build high performance 
Complere Infosystem was unable to track employee performance because its employees were not motivated or competitive, and did not take an active role in their work. As a result, business was struggling. Want to know how Complere Infosystem is now able to come out of this tough situation and improve from 50% to 80%? Please click here! 

How automated reports helps
in increasing the productivity?

Challenge

Automated reports systems help businesses track the support team’s performance, goals, & self-evaluation to increase productivity and growth.

Creating the proper automated report process for the organization is a challenge. Complere has to build an automated report process to track team performance, each member’s self-evaluation, their goals, and increasing their productivity. It’s an entirely automated process to build a continuous connection between employer and employee.
Complere has to build an automated report process for a client to increase the productivity and performance of team members.

What does Complere do?

Complere set up an automated email system with metrics to track team members’ performance, goals, and daily targets. First, complere a scripted email and send it to their team members for feedback. Later all emails are sent to the owner automatically to track the performance and productivity of team members.

Complere create a metric for self-evaluation of team members. For example, we created a scripted email with giving metrics below:

Q – How many inbound calls have you made in a day?
Q – How many outbound calls have you made in a day?
Q – What is the Total call Duration of a day?
Q – What is the Total inbound call duration of a day?
Q – Total number of calls in a day?
Q – Average Handling Time?
Q – Average Response Time?

By asking these questions to their team members, each member can analyze daily targets and work progress. After sending feedback, all data is directly sent to the owner. This automated email system built a strong connection between employer and employee to keep track of team members’ performance, goals, and productivity.

The Scope of the engagement included the following initiative.

  • Build a complete automation to track the performance and productivity of team members
  • Creating metrics to do self-evaluation of each team member
  • Build a continuous connection between employer and employee
  • The owner keeps a track record of each team member’s performance.
  • Help to analyze the productivity and growth of the business.
  • Each team member does self-evaluation daily and sends a report to the owner.

Complere Solution benefited in many ways:

  • Complere provides a complete automated system to track their team members’ performance, goals, and productivity.
  • Each team member can do self-evaluation quickly by answering a few questions, and this report is automatically sent to the owner.
  • It helps to keep track of the growth of business and increase productivity.

Metrics

Complere builds an easy but effective automated system for team members to self-evaluate and send complete reports to the owner to track the performance and productivity. When team members do a self-evaluation and share the work performance with the owner, it results in high productivity. This automated system helps clients track the performance of their team members and increase the team’s productivity. We’ve built this system, so it is easy for clients to send a scripted SMS with given metrics to the team members and their feedback directly forwarded to the owner to track the daily targets, performance, goals, and productivity in work.

How to validate the data after the Transformation

If you want to make a result-driven decision, high-quality data should be present in your business. But the biggest challenge is how to measure quality data in your firm. Don't worry; we will give you a result-driven plan, which, once in practice, will reduce the flaws of any project, high-quality filter data, and the process will promote effectiveness. 

At Complere, we use a reliable cross-checking data validation process, ensuring that the dataset is accurate, clean, reliable, correct, and complete by removing data mistakes from any project and simple to data handling. 

Let's get into further detail.

How to validate data in your organization?

In our high school, we performed cross-checking to determine whether the results were accurate. When validating your data, we can similarly employ a cross-checking method. 
Here, an approachable and interesting example will help us understand: 

Interesting validate data example

 

Imagine you have 100 rupees. And you wish to divide this sum among five people, ensuring that the amounts are distributed equally. 
Therefore, you have divided 20rs in this situation for each individual. To arrive at the right answer, you used the solution approach 100/5 = 20. 

Cross-check approach in Data validation

 

Implement the cross-check approach described below to ensure you’ve given each person the appropriate amount of 20X5=100. 
You are now sure that you distributed the same amount to each participant. 

How to implement cross-checking approach in data validation?

 

The cross-checking approach can be applied similarly to data validation. Want to know how? Please continue the reading. We’ll talk about a compelling example to help you comprehend the cross-checking method more clearly. 

Compelling examples to apply a cross-checking approach in data verification

 

Let’s say you want to know how many subscribers you have for your social media campaign. Then you must create a straightforward query for it, such as group by, or if you want to perform a cross-check, add up all the subscriptions to determine. They are consistent with the initial subscription.  

When you cross-check and validate the data, you may be sure that you have successfully loaded or transformed the data by comparing the source and what has been loaded in the target. 

Benefits of cross-checking data validation in your business

 

We know that inaccurate data costs the business time, money, and resources. Therefore, having high-quality data is essential for accuracy and dependability. The benefits of data validation in your business are listed below: 

  • Data validation ensures that the data in your system is accurate. Your business benefits from accurate data in many different ways, especially when it comes to sales. 
  • Without question, sales teams rely on reliable data to create and maintain accurate sales lead lists. Your sales funnel won’t be able to stay successful to fill pipeline full. If you keep employing disconnected lines or expired email addresses. 
  • Businesses save time and create many potential possibilities by authenticating data. 
  • Data validation ensures that you work with accurate data for your current clients, organizational structures, executive directories, and financial information. 

Complere can help

Complere combines the most advanced automated technologies with skilled, experienced personnel to give you the best data validation services available. 

We understand that it is not possible to have your personnel manually validate information every day. We can swiftly and accurately authenticate data using industry-leading procedures, giving your employees access to the most recent, accurate, and comprehensive information whenever they need it. 

Call the Complere team at 7042675588 today to learn more about our data validation services and how we can help you.

Why Data warehouse is needed

Have you ever considered the need for a data warehouse in your company? Regardless of the industry, you are in. If not, then you will benefit from reading this article. There is a "single source of truth" data warehouse for all of your company's data.  

You can make data-driven decisions using a data warehouse and act accordingly to meet your business milestones. 

Let's evaluate why setting up a data warehouse is essential for your company.

Top reasons to use Data Warehouse

  • Visual dashboard: You can use it to create a visual dashboard. 
  • Boardroom presentations: You can create boardroom presentations by downloading them in PowerPoint or PDF. 
  • Secures and protects: It secures and protects classified information from multiple sources. 
  • Fast access: ETL (Extract, Transform, Load) ensures fast access to data for the data warehouse. 
  • Save time and increase ROI: The shadow system reduces time, increases knowledge, and increases efficiency. As a result, the ROI of the companies increases. 
  • Design real-time reports: You can create real-time reports and learn more about your business data using several technologies. 

Business and data analyst teams previously used common databases to conduct SQL queries for analytical purposes. However, as time went on, businesses were producing more data and using it for more analysis to help them make business decisions.  

But as a result, the query processing speed of your specific database becomes incredibly slow. Businesses then recognized the value and need for a data warehouse. The truth is that it’s built to handle vast amounts of data; it’s astonishing. It enables data analysts to filter, sort, and aggregate and analyze data quickly.  

How can your business benefit from a data warehouse? 

It can help to reduce and eliminate the following problems: 

Data inconsistency: It gets rid of inconsistent data. Data inconsistency occurs when a database contains many tables that may have the same data because the data came from various sources.

It reduces data quality issues in your organization: If your organization has poor data quality, it isn’t accomplishing its goals and losing credibility. Data from many sources may have problems with data quality, but data warehousing will help to address these problems. 

It helps reduce your queries’ low performance: Low query performance can have an expensive effect on systems. Low-performance queries may hold transactions and locks for more extended periods and have a higher likelihood of blocking and more client application timeouts. But with the Data Warehouse, you can make more precise improvements. 

It eliminates the unstable data in your reports: Even if you make a minor report change, the result of the calculation still changes significantly. The data warehouse will eliminate this kind of inaccuracy faster. 

A data warehouse can help run analysis on a vast volume of data: You can use a significant amount of data from a data warehouse for data analysis. For quick query response time, it should be optimized. Hence it is necessary to renormalize the data. 

I believe you have fully understood the need for a data warehouse in your company. Give us a call or send your inquiry if you need help implementing a data warehouse in your company. Our experts will assist you immediately. 
Or 
If you want to know how Complere uses a data warehouse and what its benefits are. Follow us on our page.

Are you facing performance issues
with your reports? (part2)

It is our part 2 article on issues you need to take care of while designing your reports. So, today in this article, we will discuss three issues and their solutions that you face while designing your reports.  

Data storage, integration, and confidentiality are essential for any successful business to allow for easy, quick, and remote access. Regardless of the success, good performance reports with high-quality data are one of the key building blocks for managing any business successfully. It holds for both management and customer service.

A good big data management report is the success secret that enables you to compete in a globalized market. The better a firm handles its data, the better able to respond to market demands and rivals.   

 

A good big data management report is the success secret that enables you to compete in a globalized market. The better a firm handles its data, the better able to respond to market demands and rivals.  

Let’s start by talking about the three problems you have when developing your reports: 

ISSUE 1 - Which is best for designing a report transactional database (DB) and data warehouse?

 

So, in this article, we will discuss that you should go for the data warehouse instead building your reports in a transactional database (DB).  

Solution:
When developing your reports, deciding between a data warehouse and a transactional database (DB) may be challenging. Then, our post will provide a clear understanding of selecting a data warehouse for report creation. To create your reports, let’s examine the differences between a transactional database (DB) and a data warehouse. 

Transactional database (DB)

 

  • A transactional database (DB) can assist you in processing transactions quickly. 
  • You can utilize a transactional database (DB) to produce reports if you have a few sources. 
  • Its ability to integrate and analyze data is rather constrained. 
  • There is no doubt that it provides quick implementation and is inexpensive for you. 
  • Given the company’s current situation, it is only prudent.

Data warehouse 

  • It can store endless data sources in a data warehouse. 
  • It enables the analysis and collection of enormous amounts of data. 
  • Using it, you can easily extract reports from complex data and visualize the data. 
  • Initialization requires a lot of work and is expensive to implement. 
  • You can include mid and long-term projections in your reports. 

After learning how it differs, I hope you now understand why to use a data warehouse for reporting. 

Issue 2 - You must not do multiple, full outer joins or cross joins or cross tabs

 

The second thing, we need to make sure that while designing your reports, you should not have multiple, full outer joins or cross joins or cross tabs. It slows down the performance of your reports. 

Solutions: 
The performance of your reports will slow down as we build them and perform full outer joins to combine the tables. CROSS JOIN is used to merge each row of one table with each row of another, but it slows down the running speed of your report. With cross tab, we analyze only nominal measurement scales. 

Issue 3 - Your database should be optimized

Solutions: 
It should optimize your database: So, you should use partitioning or indexing in your database, which will help improve your database and you’re reporting as well!  
Making sure your database is optimized is essential. Therefore, it is a sensible decision to partition, which enables you to divide a single database table into pieces and store data into several files or you can use indexing, which allows you to run efficient queries and enhances your database and reporting performance! 

Final words: 
Three significant problems that typically arise during design reports have been covered, along with their solutions. Please visit our website or call us if you need assistance with the design of your report or how to enhance its performance. The DBA team will give you a callback. 
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If you’re interested in how we create successful performing reports, check out our page.

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