“The goal is to turn data into information, and information into insight.”
Imagine if big data was a movie titled Big Data – The Superheroes, where data analysts are the heroes and data architects the directors. But of course, you also need someone to take care of the lighting, camera, and build the movie set. The equivalent of this in IT is ETL—a difficult but important part of moving and molding data into a usable form that is demanded by analysts and architects.
An unsung part of DWH, ETL’s role is much larger and challenging than you think.
ETL or Extract, Transform, and Load is the process of moving data from the source into a data warehouse.
In this blog post, we’ll talk about the things you must consider while choosing an ETL tool and also differentiate a few popular ones. Let’s dive in!
Things to consider while choosing an ETL tool:
- Find a tool that you can easily connect to all the data sources you use and which is able to fetch all the data you need.
- Ensure that the tool fetches all data easily and efficiently and is also easy to install and maintain in-house.
- The chosen tool should be cost-effective, must have high-security standards, and should have the required functionalities and capabilities.
Top ETL Tools:
Over recent years, an array of vendors have flooded the market with numerous ETL tools. In this blog post, we’ll be comparing the top 5 tools—Alteryx, SQL Server, Python, Informatica, and Talend.
1. Alteryx – Specializing in self-service analytics, Alteryx produces several data analytics solutions. The company is known to combine ETL database capabilities with spatial processing. The Alteryx ETL tool –
- Enables users to analyze data from multiple sources including cloud or on-premise data stores, spreadsheets, AWS, and Salesforce
- Allows accessing, blending, and cleansing data from any source
- Has a drag-and-drop user interface with zero coding requirements
2. Informatica PowerCenter – Having more than 50 global partners and 1 trillion transactions each month, Informatica is a leader in cloud data management. PowerCenter—a tool that Informatica developed for data integration—supports a huge volume of data of any type. The tool –
- Is readily available with easy training modules
- Supports data analysis and data warehousing
- Can easily be integrated with other tools
- Connects various cloud apps and also supports agile processes
3. SQL Server Integration Services (SSIS) – As one of the most popular ETL tools, SSIS has tools that you can use for APIs, data transformations, flat files, and cross-server communication. The tool –
- Has a visual interface allowing people to see the flow of data or the process of tasks
- Uses other .NET and T-SQL tools and makes everything possible—right from data flows to FTP tasks, and data transformation
- Includes and runs custom scripts and supports Microsoft Visual C# and Microsoft Visual Basic programming languages
4. Python ETL – Python-ETL is an open-source Extract, Transform, Load (ETL) library written in Python. It allows data to be read from a variety of formats and sources, where it can be cleaned, merged, and transformed using any Python library and then finally saved into all formats Python-ETL supports. The tool –
- Allows the developer to code the ETL process in Python code
- Works with both CPython and Jython
5. Talend – Talend Open Studio is a basic tool for ETL processes that works well with Redshift, Google BigQuery, Snowflake and more. It helps companies take real-time decisions by becoming more data-driven. The tool –
- Automates and maintains tasks
- Is able to monitor and manage difficult deployments easily
- Can combine, convert, and update data from multiple sources
ETL is the heart of any data warehousing project as they help you gather, clean, and load your data into the data warehousing solution of your choice. Hence, it is important to find the right tool that lets you focus on deriving the insights your company needs.
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