Extract, Transform, and Load are referred to as ETL. ETL is the process of gathering data from numerous sources, standardizing it, and then transferring it to a central database, data lake, data warehouse, or data store for additional analysis.
The ETL process transforms structured or unstructured data from numerous sources into a simple format for your employees to understand and use regularly. Involved in each step of the end-to-end ETL process are:
1. Data extraction
Data that has been extracted has been retrieved from one or more sources, both structured and unstructured. These sources include websites, mobile apps, CRM platforms, on-premises databases, legacy data systems, analytics tools, and SaaS platforms. The data loads into a staging area after retrieval is finished and is ready for a transformation.
2. Data transformation
The transform stage prepares the extracted data for storage on the database, data store, data warehouse, or data lake of your choice by cleaning and formatting it. The objective is to get the data ready for querying in the target storage.
Moving prepared data into a target database, data mart, data hub, warehouse, or data lake is known as loading. Data can be loaded in two ways: gradually (incremental loading) or all at once (total loading). The data can also be scheduled to load in batches or loaded in real time.
Incremental data loading eliminates duplication by comparing incoming data to existing data. Every item that exits the transformation assembly line is transported to the final warehouse or repository during a total loading.
What Do ETL Tools Do?
The entire ETL procedure is automated using an ETL tool. ETL solutions employ several data management strategies to automate the extraction, transformation, and loading (ETL) process, reducing errors and speeding up data integration.
And there’s more. Use cases for ETL tools include:
- Automate the processing, management, and intake of enormous amounts of structured and unstructured data locally and on the cloud.
- Securely deliver data to an appropriate analytics location.
- Placing them in a historical perspective may make assessing, evaluating, and comprehending current and historical datasets simpler.
- Replicate databases to a cloud data warehouse from sources like MongoDB, Cloud SQL for MySQL, Oracle, Microsoft SQL Server, and AWS RedShift. ETL tools can be used to update your data periodically or continuously.
- Your on-site data, applications, and workflows should be moved to the cloud.
- Transfer data from numerous IoT devices to a single location so you may further examine it.
- For a more thorough analysis, combine data from social networks, online analytics, and customer service in one location.
Most Popular ETL Tools:
A data warehouse integration platform created for e-commerce is called Integrate.io. Integrate.io assists e-commerce businesses in creating a 360-degree perspective of their customers, creating a single source of truth for data-driven choices, enhancing consumer insights through improved operational insights, and boosting ROI.
Skyvia is a cloud data platform created by Devart that enables no-coding data integration, backup, management, and access. The Devart company is a well-known and reliable supplier of data access solutions, development tools, database tools, and other software products, with over 40 000 grateful clients in two R&D departments.
With support for CSV files, databases (Oracle, SQL Server, PostgreSQL, MySQL), cloud data warehouses (Google BigQuery, Amazon Redshift), and cloud apps (Amazon Redshift, Google BigQuery), Skyvia offer an ETL solution for diverse data integration scenarios (HubSpot, Salesforce, Dynamics CRM, and many others).
An online SQL client, a cloud data backup tool, and an OData server-as-a-service option are also included.
The ‘affordable speed-in-volume’ value of Voracity’s underlying CoSort engine and its robust built-in data discovery, integration, migration, governance, and analytics features have made it a popular on-premise and cloud-enabled ETL and data management platform.
Voracity supports hundreds of data sources and immediately feeds BI and visualization targets as a “production analytic platform.”
Users of the Voracity platform can create a batch or real-time operations that integrate previously optimized E, T, and L activities or “speed or leave” a current ETL solution like Informatica for reasons of price or performance. The speed of Voracity is comparable to Ab Initio, although Pentaho is more expensive.
Dataddo is a cloud-based ETL platform that requires no coding and offers flexible data integration for technical and non-technical users. With a large selection of connectors and fully customizable metrics, Dataddo makes the process of building data pipelines simple.
Dataddo seamlessly integrates with your current workflows and your data architecture. Thanks to its user-friendly interface and straightforward setup process, you can concentrate on integrating your data, and fully-managed APIs eliminate the need for ongoing pipeline maintenance.
A data ETL solution for on-premises and cloud databases is DBConvert Studio. It extracts, transforms, and loads data between many database formats, including cloud data from Amazon RDS, Amazon Aurora, Microsoft Azure SQL, Google Cloud, Oracle, MySQL, MS SQL, PostgreSQL, MS FoxPro, Firebird, SQLite, MS Access, and DB2.
To fine-tune migration options and start conversion or synchronization, use GUI mode. Schedule the execution of the command-line method saved jobs.
One-way or two-way data migration and synchronization are both possible. Initially, DBConvert studio establishes concurrent connections with databases. The migration/replication process is then tracked by a different job that is created.
Database objects and structures can be copied with or without data. Each item can be checked over and adjusted to avoid any potential mistakes.
Data management with over 500 international partners and over a trillion transactions monthly. It is a software development company with its headquarters in California, the United States, established in 1993. It generates $1.05 billion in revenue and employs about 4,000 people.
Informatica created the PowerCenter product as a means of integrating data. PowerCenter combines a large volume of data from any source and with any data type. It gives vital data and benefits to the organization while supporting the data integration lifecycle.
IBM is a global software business founded in 1911, with its headquarters in New York, the United States, and offices in more than 170 nations. As of 2016, it has a $79.91 billion annual revenue and 380,000 employees.
The IBM product Infosphere Information Server was created in 2008. It is a pioneer in data integration platforms, supporting understanding and delivering strong business values. Large-scale businesses and Big Data firms are its primary target market.
Oracle was established in 1977 and is an American multinational corporation with its headquarters in California. As of 2017, it has 138,000 employees and a total revenue of $37.72 billion.
A graphical platform for creating and managing data integration is called Oracle Data Integrator (ODI). It is a complete data integration platform that supports SOA-enabled data services and actual volume data. Large enterprises with regular migration needs should use this product.
Microsoft Corporation is a 1975-born, Washington-based American multinational corporation. It has a workforce of 124,000 people and an annual revenue of $89.95 billion.
Microsoft created SSIS, a solution that was made for data migration. As the integration and transformation of the data are handled in memory, the data integration is much faster. SSIS only supports Microsoft SQL Server because it is a Microsoft product.
Ab Initio is a private American software company with offices in Japan, France, the UK, Poland, Germany, Singapore, and Australia that was founded in 1995 and is based in Massachusetts, USA. High-volume data processing and application integration are two areas of expertise for Ab Initio.
Six data processing tools, including the Coordinating System, The Component Library, Data Profiler, Graphical Development Environment, Enterprise Meta environment, and Conduct It. “Ab Initio Co>Operating System” is a drag-and-drop enabled GUI-based ETL tool.
Talend is a software company with US headquarters in California that was established in 2005. Approximately 600 people work for it presently.
The business’s initial offering, Talend Open Studio for Data Integration, was released in 2006. It is a platform for data integration that facilitates data monitoring and integration. The business offers services for data management, data preparation, corporate application integration, and other data-related tasks. Data warehousing, migration, and profiling are supported.
The most challenging data management difficulties worldwide are assisted by CloverDX for midsize to enterprise-level businesses.
With its powerful developer tools, scalable automation, orchestration backend, and robust yet infinitely adaptable environment built for data-intensive operations, the CloverDX Data Integration Platform offers businesses.
Since its founding in 2002, CloverDX has grown to a staff of more than 100 individuals, including developers and consultants from various industry sectors who work globally to help businesses master their data.
Software provider Pentaho sells Pentaho Data Integration (PDI), also referred to as Kettle. Data integration, mining, and STL capabilities are among its services. Its corporate office is in Florida, USA. Hitachi Data System purchased Pentaho in 2015.
With the help of Pentaho Data Integration, users may clean up and prepare data from diverse sources and move data between applications. A component of the Pentaho business intelligent package, PDI is an open-source technology.
The American state of Maryland is the home of the Apache Software Foundation (ASF), established in 1999. Under the terms of the ASF, it creates free, Apache License, open-source software. The Apache Software Foundation is the organization behind the Apache Nifi software project.
Through automation, Apache Nifi makes it easier for data to move across different systems. The processors that make up the data flows can be customized by the user. These flows can be archived as templates, which can then be combined with more intricate flows in the future. Then, these complicated flows may be deployed to numerous servers with little effort.
SAS Data Integration Studio is a graphical user interface for creating and managing data integration processes.
The data source can be any platform or app for the integration process. It includes strong transformation logic that allows developers to create, plan, execute, and track jobs.
The data integration and ETL tool are called BusinessObjects Data Integrator. Data integrator Designers and Job Servers make up most of it. The Data Integration process for BusinessObjects is broken down into four steps: Data profiling, Data unification, Data auditing, and Data cleaning.
Data can be taken from any source and put into any data warehouse using SAP BusinessObjects Data Integrator.
Oracle Warehouse Builder is an ETL tool that Oracle has released (OWB). The data integration process is built and managed via a graphical environment.
For integration reasons, OWB uses a variety of data sources in the data warehouse. Data profiling, data cleaning, fully integrated data modeling, and data auditing make up the critical competencies of OWB. OWB connects many third-party databases and transforms data from numerous sources using an Oracle database.
Jaspersoft, a pioneer in data integration, was established in 1991 and has its US headquarters in California. It takes data from multiple sources, extracts, transforms, and loads it into the data warehouse.
The Jaspersoft Business Intelligent suite includes Jaspersoft. A platform for data integration with high-performing ETL capabilities is called Jaspersoft ETL.
To keep all of their data in one location, marketers can use the data analytics program Improvado. You may link marketing API to any visualization tool with this marketing ETL platform without having any technical knowledge.
It can link to more than 100 different kinds of data sources. These data sources will be able to be connected to and managed by a single platform, whether it is located on-site or in the cloud. It offers a selection of connectors for attaching to data sources.
For cloud data warehouses, Matillion is a data transformation solution. To swiftly combine substantial data sets and carry out the essential data transformations that prepare your data for analytics, Matillion makes use of the cloud data warehouse’s capability.
This system is specially made to pull data from diverse sources, load it into a business’s preferred cloud data warehouse, and then transform that data at scale from its siloed condition into accurate, joined-together, analytics-ready data. It works with Amazon Redshift, Snowflake, and Google BigQuery.
High-performance business intelligence and ETL procedures are carried out using IBM Cognos Data Manager.
It has a unique characteristic of multilingual support, which it can use to build a global platform for data integration. The Windows, UNIX, and Linux platforms are supported by IBM Cognos Data Manager, which automates business processes.
ETL tools include the Pervasive Data Integrator tool. A rapid connection between any data source and application is beneficial.
It is a robust platform for data integration that facilitates real-time data movement and interchange. The tool’s components can be reused and deployed as many times as necessary because they are reusable.
Prathamesh Ingle is a Consulting Content Writer at MarktechPost. He is a Mechanical Engineer and working as a Data Analyst. He is also an AI practitioner and certified Data Scientist with interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real life applications