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Data Engineering
Services

Fuel data-driven innovations with our data engineering services.

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From Data Deluge to Data-Driven

From Data Data-Driven

From Data Deluge to
 Data-Driven

Information systems in organizations evolve in response to different department needs at different points in time. Each system has a different view of enterprise data, which combined can bring even more value in the short and the long term. Through data engineering, we make this data accessible for downstream applications and enterprise decision-makers.

Data engineering has also emerged as an important discipline in the wake of the data deluge of the digital era and the advent of cloud-based frameworks and tools to manage them. We engineer systems that give you a firm handle on this data and channel it for business intelligence.

Architecting and Managing Data Pipelines

Data Pipeline

Architecting and Managing
Data Pipelines

Moving data from one system to another is inherently daunting, but getting it right is integral to deriving value out of accumulated data. Our data engineers design and manage systems to ensure high throughput and availability of data from diverse systems. Through data transformation operations, we normalize data from disparate systems and check for quality and consistency. A good design ensures the fail-proof operation of your data pipelines.

With massive amounts of data coming from sources as varied as SQL, NoSQL, S3 buckets, and streams, scalable pipelines with extensible processing capabilities need to be developed to ensure a reliable data flow. Based on the use case, ingested data is deposited in a data warehouse or data lake for storage or further processing downstream. While automation takes care of a large part of the CI/CD operations, our engineers play the vital role of monitoring and fine-tuning systems for cost-effective performance.

Processing Data at Speed

Processing Data

Processing Data at Speed

Faster response times are the norm for applications such as online trading and IoT whereas for applications like order fulfillment and payroll processing, a certain amount of latency is acceptable. Given these differences, tools, frameworks, and hardware have to be carefully planned. And the answers are not always self-evident as trade-offs are involved, which is why the support of an experienced data engineering team is an advantage.

System manageability is another key consideration; with distributed systems, integration of the various components, such as relational and non-relational databases, virtual machines, containers, etc., maintaining a secure and reliable computing environment is essential. Our team creates the road map for data processing and management based on the use case, considering factors such as the nature of data, desired response time, and type of application.

Two Approaches in Cloud Architecture

Clustered and serverless are two models for architecting cloud infrastructure. We decide on an approach based on the frequency and variability of data processing.

Server-Based

A group of interconnected servers or clusters take up the same requests and ensure service availability even if one fails. Cloud enables dynamically changing cluster capacity to meet changing computational demands. Pricing-wise cheaper when there is consistent high-volume demand.

Serverless

A cloud-native model where, instead of servers, the cloud provider enables the on-demand creation of computational processes (thus “serverless”). There is no dedicated resource involved; resource is invoked on demand. Serverless is cost-effective for discrete workloads and infrequent demand.

Server-Based Serverless
A group of interconnected servers or clusters take up the same requests and ensure service availability even if one fails. Cloud enables dynamically changing cluster capacity to meet changing computational demands. Pricing-wise cheaper when there is consistent high-volume demand. A cloud-native model where, instead of servers, the cloud provider enables the on-demand creation of computational processes (thus “serverless”). There is no dedicated resource involved; resource is invoked on demand. Serverless is cost-effective for discrete workloads and infrequent demand.

Building the Enterprise Data Infrastructure

Databases, data lakehouses, data warehouses, and data marts—one or more of these systems and subsystems make up the complex enterprise data ecosystem. Orchestrating such a complex system, and ensuring availability, performance, and security requires skills spanning multiple disciplines including architecture design, cloud development, networking, and security. Our data engineers bring to bear their experience in a wide array of enterprise applications, legacy systems, big data tools, and frameworks to execute your big data ecosystem.

Databases

NoSQL databases are changing how data is stored and processed. Optimized for different data types, they take pride of place along with RDBMS.

Data Lakes

Ideal storage for huge amounts of raw data. With self-service options, data lakes can help advance the long-time analytic needs of your enterprise.

Data Warehouses

With high fault tolerance and massive scalability, cloud is changing how the data warehouse, a repository for processed data, is architected and managed.

Data Lakehouses

Data Lakehouses combine the flexibility and scalability of data lakes with the reliability and low-latency query performance of data warehouses.

Data Fabric

Data fabric integrates disparate data sources and formats into a cohesive framework, enabling seamless data access and management.

Data Mesh

A decentralized approach to data architecture, data mesh focuses on domain-oriented data ownership and collaboration through domain-specific data products within an overarching governance framework.

Data Marts

Data marts are a simpler version of the data warehouse with highly focused data for specific lines of business, such as finance and marketing.

Databases

NoSQL databases are changing how data is stored and processed. Optimized for different data types, they take pride of place along with RDBMS.

Data Lakes

Ideal storage for huge amounts of raw data. With self-service options, data lakes can help advance the long-time analytic needs of your enterprise.

Data Warehouses

With high fault tolerance and massive scalability, cloud is changing how the data warehouse, a repository for processed data, is architected and managed.

Data Lakehouses

Data Lakehouses combine the flexibility and scalability of data lakes with the reliability and low-latency query performance of data warehouses.

Data Fabric

Data fabric integrates disparate data sources and formats into a cohesive framework, enabling seamless data access and management.

Data Mesh

A decentralized approach to data architecture, data mesh focuses on domain-oriented data ownership and collaboration through domain-specific data products within an overarching governance framework.

Data Marts

Data marts are a simpler version of the data warehouse with highly focused data for specific lines of business, such as finance and marketing.

Microsoft
amazon
snowflake
Google

Stages in Data Engineering

Stages in Data Engineering

Our Strengths

Engineering Expertise

Strong programming skills, extensive, hands-on experience in SQL and NoSQL, big data frameworks and platforms including Azure, AWS, GCP, etc., data processing tools such as Spark, Lamda, MapReduce, Elasticsearch, and data visualization tools.

Cross-Industry Experience

From top-tier automobile companies to e-commerce giants, we have backed many a heavyweight in their data engineering projects. We have built a formidable knowledge base catering to different industries, each with its own distinct data models, applications, and data processing needs.

Multidisciplinary Team

On our team, we have data architects with over a decade’s experience to design well-governed data ecosystems, data engineers to build data infrastructure from the ground up or migrate to cloud platforms, and data scientists with machine learning and data visualization skills.

Engineering
Expertise

Strong programming skills, extensive, hands-on experience in SQL and NoSQL, big data frameworks and platforms including Azure, AWS, GCP, etc., data processing tools such as Spark, Lamda, MapReduce, Elasticsearch, and data visualization tools.

Cross-Industry
Experience

From top-tier automobile companies to e-commerce giants, we have backed many a heavyweight in their data engineering projects. We have built a formidable knowledge base catering to different industries, each with its own distinct data models, applications, and data processing needs.

Multidisciplinary
Team

On our team, we have data architects with over a decade’s experience to design well-governed data ecosystems, data engineers to build data infrastructure from the ground up or migrate to cloud platforms, and data scientists with machine learning and data visualization skills.

Quality Decisions Require Quality Data

Quality Data

Quality Decisions Require
Quality Data

Decisions are only as good as the data underlying them. Inconsistent formats, missing values, and invalid data affect data integrity, making it less of a valuable resource. Our data engineers execute quality management processes to ensure data hygiene and reliability. Learn more about our data management approach to BI.

Data Engineering Is Only the First Step

Data engineering is not an end

Data Engineering Is Only
 the First Step

Data engineering is not an end in itself but the beginning of a promising quest for insights and innovative solutions to new and existing challenges. Read about our data science approach to problem-solving.

Resources

ELK-based Analytics Solution

View Case Study

ETL Solution for Telecom

View Case Study

Data Analytics and Warehousing

View Case Study
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