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Things to Understand Before Implementing ETL Tools

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Things to Understand Before Implementing ETL Tools

In this post, we quickly go over the benefits and challenges that ETL tools will bring to your data teams and data pipeline.

· Big Data Zone ·
Free Resource

Data warehouses, databases, data lakes, or data hubs have become key growth drivers for technology-driven businesses of all sizes. There are several factors that contribute to the successful building and management of each of these data systems. The ETL (Extract, Transform, Load) strategy is the most important of them all. Nowadays, there are several best ETL tools in the market which allow businesses to design robust data systems. They are differentiated into open source and enterprise ETL tools on the basis of their implementation. This post is not focused on the best ETL tools in the market, nor does it compare ETL tools. What should you expect then? This post intends to build your understanding of the ETL processing and parameters to be checked before investing in an ETL tool.

Understanding the Basics of the ETL Processing

When developing a database, it becomes important to prepare and store data in comprehensible formats. ETL comprises three distinct functions (Extract, Transform, and Load) that are integrated in a single tool, which aids in the data preparation and storage required for database management.

Let’s discuss Extract, Transform, and Load in detail. Each of these processes helps move data from its source of origin to a storage system.

  • Extract: As the name suggests, this step involves information retrieval from various source systems such as JSON, XML, and RDBMS. The extraction of data is conducted so that there is no negative impact on the source in any way.

  • Transform: This phase involves various processes, where the extracted data is transformed into an easy-to-understand format. The steps involved in transformation are duplicate removal of entries, filtering, validation, merging data from different sources, and so on.

  • Load: The final phase of the ETL process, where the data is loaded into a data warehouse. The data loading is performed in one of the two ways — incremental loading or full loading. In full loading, all the data is moved to the warehouse at one time, whereas in incremental loading, this movement occurs in batches.

3 Parameters to Be Checked While Comparing ETL Tools

There are several ETL tools that may look as a perfect fit for your ETL project. However, considering the following points will help you make the right decision.

  1. The volume of data to be managed: Is the tool designed for data retrieval from a single source or multiple sources? This consideration is key to any ETL investment. This is because the tools used for data retrieval from a single source differ from the ones designed for data retrieval from multiple sources.

  2. Nature of data to be moved: Owing to the increasing use of digital tools, there is a lot of structured and unstructured data coming from various sources. For instance, the data retrieved from payroll, ERP, and CRM systems will be in different formats. So, they need to be processed in a format that is uniform and comprehensible for analytical and reporting tools. Thus, it is important to check if your ETL tool has the ability to transform a particular type of data produced by other tools in your organization.

  3. Tasks that a tool is expected to perform: What do you want your ETL tool to do? What systems will the tool retrieve data from and where will this data be delivered? An understanding of the type of data the tool is expected to retrieve and process, as well as the end point of the whole ETL processing project, will help you make a good decision. For instance, if you plan to process data for your SQL database then it is ideal to invest in a SQL compatible ETL tool.

In addition to the above-mentioned, there are several other parameters such as ease-of-use, the product road map, ability to process complex data, performance, operational resilience, and value for investment to be considered.

Challenges to Be Addressed When Using an ETL Tool

Although the best ETL tools in the market are designed to assure effortless data processing, there are still several challenges to be addressed during their implementation:

  1. Data extraction from unstructured sources such as websites, emails, texts, custom apps, and so on.

  2. Data loss or unavailability of data for regular business processes during the extraction. This may be due to various reasons, however, the implementation of a change data capture (CDC) is touted as one of the main reasons.

  3. Change in data formats, as well as an increase in data volume and velocity over time.

The data engineering landscape is continuously evolving. Several new technologies and languages are being added everyday. It is a common tendency for new age enterprises to get drawn to these languages and technologies. There are several great ETL tools that claim to address the diverse data processing challenges faced by organizations. However, the final decision will always be influenced by your business goals, challenges, and understanding of how an efficient ETL strategy can become an asset to your business.

Topics:
etl ,etl tools ,data engineering ,data management ,big data

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