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Data Engineering Services

Let your data create an impact with our customized, results-driven solutions. With a strong track record of success, we provide scalable, ROI-focused services that help businesses make smarter and more impactful decisions.

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A business can succeed only if it listens to the market demands and customer requirements consistently. These details are usually scattered all over social media, communities, and other social platforms. Raw data can also be gathered from various sources like websites, third-party applications, legacy systems, and more. However, turning the raw data into actionable business insights is the real game changer. This is where data engineering services come into action!

Data engineering services include many activities such as gathering, cleaning, storing, and processing data. Designing data pipelines and advanced analytics programs to craft customised data engineering solutions are also included in these services.

Though vast in scope, businesses need data engineering services to automate their workflow, streamline their operations, and build business or marketing strategies. This blog covers all the aspects of data engineering services and explains why it is imperative for businesses of all kinds!

Years of Experience

98%

Client Satisfaction Rate

110+

Project Handled

35%

Average ROI Improvement

Data Engineering: An Overview

Data engineering, a subset of data science, focuses on designing, building, and maintaining systems that facilitate smart data collection, storage, and interpretation. Data engineering services focus on converting raw data into actionable insights and trends. They ensure that the collected data is accessible, reliable, and ready for various business applications. The key parts of data engineering include:

Data Collection & Feeding

Data engineering involves collecting data from multiple sources such as web, third-party apps, databases, legacy systems, etc. and feeding them into data pipelines. This process includes simultaneous handling of raw, structured, and unstructured data.

Storage

Data engineering involves designing storage solutions like data lakes, warehouses, and databases. These solutions are designed to be scalable and secure while offering easy accessibility to various stakeholders of the organisation.

Transformation Of Data

When raw or unstructured data gets converted into a structured format, the process is known as data transformation. It makes the data clean and ready for further analysis.

ETL i.e. Extract, Transform, Load pipelines are the key components of data engineering. These pipelines retrieve large datasets from various sources and convert them into a usable format or structure. Finally, the data is loaded into a system to process it further.

Data Processing

To process massive data chunks, organisations use technologies such as Apache Spark or Hadoop. These big data technologies are capable of handling large volumes of data sets.

Data processing is not a one-step activity. It involves several activities such as collection of data, organising it, and cleaning the raw data to make it usable for analysis. So, it forms the backbone of data-driven systems and applications. Without effective data processing, it is impossible to uncover the real potential of the available data.

Depending upon the methods used for processing the data, data processing can be categorised into multiple types. When large chunks of data are processed at regular intervals, it is known as batch processing.

When data is processed in real-time, it is known as real-time processing. Distributed processing involves data processing on multiple machines to manage large data sets simultaneously.

Establishing Data Framework

One of the key tasks in data engineering involves establishing frameworks to manage and evaluate data quality. These frameworks ensure that the data is consistent and adheres to the standard data regulations.

The framework determines how the data will be managed, processed, and interpreted within a system. It serves as an architecture for maintaining consistency, efficiency, and scalability throughout the data workflows.

Documentation regarding data workflows and data governance strategies are also created during this phase. Without a proper framework, you cannot retrieve actionable insights from raw data. So, it is an important part of data engineering.

Creating Environments

Data engineers create different types of environments for efficient management of data operations. This stage involves integration of modern platforms such as Microsoft Azure and Google Cloud.

For instance, development environments allow data scientists to deploy various systems, test algorithms, and conduct various experiments with the available data. Data staging environments are created for enhancing data quality, consolidating data, and deploying data security measures.

Similarly, the production environment involves deployment of data pipelines, approved workflows, data monitoring tools, and various other technologies required for a business application or analytical requirements. This type of environment influences business decisions and strategies directly. So, it is considered to be one of the most important phases in data engineering.

Data Collaboration

The final goal of data engineers is to develop data solutions that align with your organisation’s goals. However, the various stakeholders of the business must be able to access the data sets, share their data, and communicate effectively. This is where data collaboration comes into play.

Data collaboration is simply the process of acquiring data and sharing it across the network. For instance, the sales team of your company needs to share their data with the marketing and customer service teams. With the help of collaborative tools, they can share their insights to each other and develop business strategies that are backed by solid data.

Thus, data engineering proves to be the driving force behind new-age organisations that rely highly on data. With these data engineering services, you can improve your business decisions and harness the potential of AI and other modern technologies to drive innovation.

Why does Data Engineering Play a Crucial Role in Data Analytics?

Data analytics involves extracting insightful trends and actionable insights from data. However, the data available through different sources is usually in raw format. This unstructured data may generate inaccurate trends and insights after processing. To convert the raw data into a structured format, you must create competent data frameworks.

Data engineering involves creating frameworks and pipelines to collect, clean, and structure data. A solid data infrastructure facilitates smooth data flow that plays a key role in optimising processes.

Data engineering also involves creating systems that are customised as per your organisation’s needs. Moreover, their scalable structure allows you to optimise your processes as per your growing business demands.

By extracting useful information from raw data, data engineering serves as a backbone for your data analytical works and purposes.

Why do Organisations Need Data Engineering Services to Thrive in Today’s Time?

Businesses generate data on a large scale due to online interactions, digitalisation of processes, and interconnection of IoT devices. Most of this data is available in the raw form i.e. it is not structured and is highly inconsistent as well.

With data engineering services, you can integrate, clean, sort, and structure obtained from multiple sources. The structured data can then be fed to business intelligence and analytical tools for developing data-backed business strategies.

For instance, customised data lake consultancy services help companies to store large volumes of structured and unstructured data from different sources.

With these services, you can centralise data to improve data analysis and administration. They also empower your business decisions by offering ML (Machine Learning) and other tools such as Apache Spark or Hadoop for seamless exploration and visualisation of data. Some other benefits of availing data engineering services are listed below:

1. Improves Data Quality

Raw data is often unstructured and of poor quality. Analysing such data results in vague and improper insights that are useless from a business point-of-view. Data engineering services cleanse this data and transform it into actionable insights.

2. Supports AI Integration

Several businesses are looking forward to integrating AI models into their business processes. With the use of AI tools, they tend to reduce dependency on manual efforts for repetitive tasks. AI tools have also become a dependable option for communicating with clients and delegating tasks.

However, to make correct use of AI and advanced analytics, the data needs to be of high-quality. Data engineering services convert raw data into structured form and make them suitable to be fed to predictive models, AI tools, and advanced analytics.

3. Addresses Data Compliance & Security Concerns

Data compliance and security pose grave challenges to organisations these days. With a ton of data available online, companies are forced to comply with data regulations such as CCPA, GDPR, etc.

With governance-focused data engineering services, you can create frameworks to follow data regulatory compliance. These services also help you maintain privacy and security of data generated through various data channels.

4. Breaks Siloed Data

Data silos refer to data repositories that are accessible to one department of your organisation but inaccessible to other units or teams. The siloed data cannot be merged or integrated with other data sets. This makes it inconsistent and hard to use for other purposes.

Lack of communication between different teams of the same organisation often creates data silos. With data engineering services, the data silos can be broken and integrated into a centralised system from where anyone who is a part of your organisation can access and use it. This gives you a holistic view of your company’s performance with insights regarding performance of individuals and teams.

5. Develops Scalable Infrastructure

Most organisations depend on legacy systems for their day-to-day operations. The legacy systems refer to those systems that were implemented in your organisation many years ago and the people working on it have either retired or forgotten the original reason for their implementation. So, their hardware, software, or system is pretty much outdated and not suitable for modern processes.

It is costly to maintain legacy systems and they offer limited functionality and flexibility. So, they are often incompatible with advanced applications and modern systems. With data engineering services, you can design and build scalable data architectures that develop with your organisation’s growth and expansion.

Scalable cloud-based architecture can be built using AWS, Azure, and other cloud-based platforms. They allow you to allocate and deallocate resources as per your requirements. For example, cloud-based storage offers bigger storage options as your organisation grows.

Also, these platforms help you reduce expenses by adopting models that charge only for the resources you need or use. Cloud-based architecture makes data accessible on any part of the globe. Therefore, as your company expands into other geographical locations, you don’t have to move or copy data from your standalone legacy systems.

6. Fuels Innovation

Data engineering services break the data silos to make your organisation’s data more flexible and insightful. Not only that, they integrate data from CRMs, IoT devices, ERPs, and third-party applications into a centralised system.

With advanced analytics and use of machine learning and AI tools, you can prepare data pipelines to structure raw and inconsistent data. With predictive analysis, the structured data can be converted into valuable trends and insights. Finally, the structured data can be fed to AI or ML models to develop innovative solutions for your business.

By leveraging automated systems, you can redefine business processes and optimise their efficiency by reducing manual errors. By developing smart applications that are tailored to your business requirements, you can implement strict data governance for securing confidential business data.

Firms that expertise in data engineering services also create platforms that allow you to merge all your systems into a centralised platform. This ensures that the data flow and collaboration remains seamless and consistent across teams. This automatically boosts your organisation's performance, workflow, and productivity.

In-Demand Data Engineering Services

As businesses thrive on data-enabled systems and processes, the need for highly optimised and specialised data engineering services cannot be neglected. These services help them to handle, process, and leverage their data to fulfill their evolving business requirements. Some of the popular data engineering services include:

AWS (Amazon Web Services)

Microsoft Azure

Google Cloud Platform

AWS stands for Amazon Web Services. It is a cloud computing platform that offers 200+ services for computing, storing, networking, analytics, machine learning, and data security.
The key benefits of AWS lie in its ability to scale the resources up and down as per your business needs. With its auto-scaling ability, your systems can handle traffic changes smoothly.
Compliance with regulatory standards such as HIPAA and GDPR along with powerful security features make it a great option for all types of businesses. Moreover, it integrates seamlessly with different on-site systems, third-party applications, and business intelligence tools.
Its data centres are located across the globe, making it a reliable option for businesses willing to expand in the near future. AWS includes web hosting services for applications and websites along with robust data analytical tools like Redshift and EMR.
AWS’s machine learning abilities are powered by AI tools and models. Its cloud-based recovery system allows businesses to continue their operations during sudden crises or shutdowns.
AWS also offers scalable solutions for online stores, including payment integration, hosting, business analytics, and other services. All these things make it one of the most sought-after data engineering services of the modern era.

How Did We Start?
A Brief Look Into Complere’s Journey

Complere Infosystem offers comprehensive data engineering services and innovative solutions to clients willing to leverage data to thrive, build trustworthiness, and grow in the market. We help them in building robust and scalable data infrastructures that are customised according to their unique business requirements.

When we started our journey in 2017, we had a strong and clear vision to help businesses realise the true potential of their data. We wanted businesses to use data to thrive, grow, and prosper in their respective industries. However, things weren’t so easy for us at the start.

Complere started as a small team of young, dynamic, and passionate individuals who were experts in their own field. Although we had limited resources, we worked hard to solve the challenges of our clients. Slowly but surely, as the results started showing, our efforts got recognised in the market! With time, we took on more complex challenges in the field of:

Data Engineering

Custom Application Development

Cloud Computing

Business Process Automation

Data Analytics

Data Architecture, Design, & Implementation

While growing our expertise in various technologies, we understood that data is at the vertex of business growth. So, we continue to focus on developing our expertise in the fields of data science and engineering.

Today, we are a thriving company with permanent offices in Ambala and Mohali. We believe that this is just a start for us and that we are destined for bigger and better things. Sharing technological expertise is a fun process for us as we want our clients to taste real success by leveraging actionable insights!

Our Core
Data Engineering Competencies

At Complere Infosystem, we provide a wide array of data engineering solutions and customise them as per your specific industry and business needs. Our vast experience and expertise in providing data engineering services help us to manage our esteemed clientele. Some of our core data engineering competencies are given below. Before subscribing to any data engineering services, ensure that they specialise in these core competencies:

1
Data Migration

In most organisations, the first step is to migrate data from legacy systems to modern cloud-based platforms. This step is essential to fill the gap of traditional and outdated systems with innovative data engineering solutions. It also maps the future of your data management efforts in the right direction.

Data migration involves implementation of migration strategies that secure your private information from unauthorised access, leakage, and errors. Also, the migration services must be smooth enough to minimise its impact on your daily operations. Thus, data migration services include smooth migration strategies with minimum impact on business continuity.

At Complere Infosystem, we offer fast, secure, and seamless cloud migration services. One of our success stories is described below:

While collaborating with YUM Brands, an eminent titan in the global fast-food sector, we undertook a transformative endeavor to revolutionize their data warehouse infrastructure. This ambitious project was focused on migrating their existing on-premises Netezza framework to the Redshift cloud data warehouse by Amazon Web Services (AWS).

Additionally, it necessitated the comprehensive transition of data integration logic from IBM DataStage to Talend, marking a significant leap towards modernizing YUM Brands’ data architecture. This strategic overhaul was designed to augment flexibility, minimize operational costs, and utilize Talend’s sophisticated features for data processing and integration, all while providing the unparalleled scalability and agility of the AWS cloud platform.

2
Data Activation

3
Maintaining Data Quality

4
Data Warehouse Consulting

5
Data Lake Consulting Services

At Complere Infosystem, we offer fast, secure, and seamless cloud migration services. One of our success stories is described below:

All In A Nutshell

Data will continue to be the centrefold of all upcoming innovations in the future. To steer your business in the right direction, you need sharp technical assistance along with a myriad of data engineering services. At Complere Infosystem, we bring both these things on one table. With our professional data engineers, you can merge your existing company data into a new system that will be customised as per your specific business needs.

Want to expand your business or add new verticals to it? Contact us! We will gather all the data from trusted data sources to help you develop a comprehensive business strategy!

Want to redefine your user experience? You are just a step away from personalising customer experiences as we set up an omnichannel platform for customer communication and retention! Get all the actionable insights and data analytics on a single platform with user-friendly features and interactive dashboards. Visit our website today to know everything related to our data engineering services and more!

Frequently Asked Questions

Our key data engineering services include:

Developing data pipeline to collect and process data
Developing data warehouses and data lakes for storing data
ETL (Extract, Transform, and Load) data from different sources for analytics
Integrating various cloud services, databases, and data sources on a common platform
Developing data structures for querying
Maintaining data accuracy and consistency
Implementing Spark or Hadoop to handle large data sets

To manage complicated data integration tasks, we analyse the data sources and understand your business objectives keenly. Then, we work on the suitable integration pattern (ETL, Real-Time Processing, or ELT) and tools required for achieving your goals. At the start, we design flow diagrams and set up data pipelines to extract, transform, and load the data. Finally, we use tools such as Azure Data Factory, AWS Glue, etc. to handle complex data integrations while maintaining data quality and consistency.

Only professionals can manage the risks and maintain compliance with the industry standards and regulations. Also, professional help is a must to avoid data loss and data integrity problems. So, it is always wise to seek professional help or partner with a data engineering company to handle your data migration.

Yes, we design data engineering solutions that help your data infrastructure to grow as per your business needs. We build dynamic and scalable solutions that allow you to expand storage, add new data sources, and modify the processing capabilities according to your changing business needs.

Organisations that require to handle large data sets to make data-driven decisions often require data engineering services. It helps them convert raw data into actionable insights that boost their growth and innovation strategies.

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Complere Infosystem is a multinational technology support company that serves as the trusted technology partner for our clients. We are working with some of the most advanced and independent tech companies in the world.

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