Currently, the amount of data created every day is estimated at 1.145 trillion MB per day. Statista forecasts the global data creation to grow from 64.2 zettabytes to over 180 zettabytes by 2025. Regulating such a large volume and variety of data is complex. To overcome such complexities, businesses are adopting the concept of Data Engineering to help them analyze data and plan their project schedules, investments, and team decisions.

What is Data Engineering?

In layman’s terms, data engineering can transform processes, optimize tooling, and utilize innovations to store information by building systems to collect, process and analyze data. By using data engineering, organizations can identify loopholes, reduce risks, improve performance, secure investment, and plan schedules. Various ETL (Extract, transform, and load) Tools form the building blocks for a successful data engineering process.

ETL & ETL Tools

ETL denotes a process that extracts, transforms, and loads data from multiple sources to organization’s data lakehouse or other repositories for analysis. It cleanses and organizes data to achieve a business purpose.

Fig. 1. ETL Process

ETL tools play a vital role in enabling the above phases. They minimize human interference with the data, eliminating the need of regular coding, which allows for quick implementation and faster results. The selection of the right ETL tool is based on parameters such as ease of use, support and maintenance, built-in integrations, cost, and in-house development capabilities for customization.

These tools can be grouped into four categories

• Enterprise-Grade Tools
• Open-Source Tools
• Cloud-Based Tools
• Custom ETL Tools

Enterprise-Grade Tools

These tools are designed especially for large organizations. The organization can input data from various data points, such as the CRM, into a centralized data source for analysis and report generation. These tools can perform advanced data transformational activities and handle ample data in minimal time.

• IBM DataStage: An AI-powered data integration tool that offers hundreds of out-of-the-box, pre-built, ready-to-use connectors and on-premises deployment. It can be upgraded to Cloud Pak for Data to enable a hybrid or multi-cloud environment, saving data integration time and costs.
• Oracle Data Integrator: A platform that offers a graphical environment to build and manage all data integration processes. It supports requests from high-volume/high-performance batch loads to service-oriented architecture data services. ODI supports parallel task execution to enhance data processing speed and offers built-in integration with Oracle’s Golden Gate and Warehouse Builder.
• Fivetran: An automated data movement platform that offers 24/7 support, database replication, and data security services. It provides automatic and continuous updates of pipelines, allowing businesses to focus on data insights instead of ETL.
• Informatica PowerCenter: Offers AI-powered data integration along with a cloud-native architecture. It can deliver data on-demand, real-time, or Change Data Capture (CDC). Power Center analyzes and automatically validates advanced data formats. It also offers pre-built transformations and optimized performance to scale with high availability.

Open-Source Tools over

The years, many organizations or individual software developers have released free-to-use Open Source ETL tools in the market. One can easily access the source code of these tools and extend or enhance the code as per their requirements. However, even after enhancing and extending the code, these tools might struggle to meet quality expectations, ease of integration with existing data sources, ease of use, and adoption by teams.

• Talend Open Studio: An open-source tool designed to rapidly build data pipelines. It has built-in connectors to pull information from diverse environments, including RDBMS systems, SaaS platforms, and packaged applications.
• Pentaho Data Integration: Manages data integration processes, including capturing, cleansing, and storing data in a standard format. PDI shares this information with end users for analysis and supports data access for IoT technologies to facilitate machine learning.

Cloud-Based Tools

These tools manage data through various cloud-based platforms, deploying the infrastructure suggested by the organization. They help process large volumes of data without requiring additional infrastructure to store the data.

• AWS Glue: A cloud-based event-driven ETL tool that simplifies pipeline development and provides additional functions, such as the AWS Glue Data Catalog to manage data in a centralized data catalog and the AWS Glue Studio to visually create, execute, and monitor ETL pipelines to load data into your data lakes. It supports custom SQL queries for easy data interactions.
• Azure Data Factory: A fully managed, serverless data integration service built on a pay-as-you-go model. It offers visual integration of data sources with more than 90 built-in connectors and integrates with Azure Synapse Analytics for advanced data analysis. The platform helps build ETL and ELT pipelines code-free and supports Git for continuous integration/deployment workflows.
• Google Cloud Dataflow: A fully managed data processing service built to speed up streaming data pipeline development and analytics. It focuses on reducing costs through horizontal autoscaling of resources. It also offers AI capabilities for predictive analysis and real-time anomaly detection.
• io: A highly scalable no-code data pipeline platform with a robust offering (ETL, ELT & CDC, API Generation, Observability, Data Warehouse Insights) and hundreds of connectors to build and automate secure pipelines in minutes. The platform helps provide constantly refreshed data for actionable, data-backed insights to reduce your CAC and increase your ROAS to achieve business goals.
• io: A no-code ETL tool that can be used without technical skills. Coupler enables businesses to fully leverage data and provide an all-in-one data analytics and automation platform. It enables the export and blending of data from various business applications to data warehouses or spreadsheets to create live dashboards to track business metrics.

Custom ETL Tools

Multiple businesses use general-purpose programming languages to write their own ETL tools. This offers businesses the benefit of customization and designing the tool to efficiently achieve their business objective. However, designing a customized tool in-house requires tremendous effort and is time-consuming with a high cost of development. It also becomes difficult for custom ETL tools to be used across teams. Due to these reasons, most companies prefer existing tools or outsourcing the development and maintenance.

Data engineering can redefine the entire work ecosystem by creating, capturing, and integrating data using various ETL tools. It helps companies make better and more informed decisions by simplifying the transformation of data received from multiple sources into valuable insights.

With a continuous focus on customer experience and data-driven product enhancements, the need for data transformation has rapidly expanded across organizations and has become the core of today’s successful businesses.

eInfochips has strong cloud partnerships with Microsoft, AWS, and Google Cloud. We have leveraged our partnerships to deliver ETL packages and data integration services to our clients, helping them gain an enterprise-wide view and insights, resulting in increased efficiencies in operation, resources, and projects.

To know more about our offerings, please talk to our experts.