In the late 1970s, storing business information in the form of data repositories or databases became a common sight. This resulted in the need to integrate data, spread across databases. To address the challenge, in the early 1990s, Data Warehouses came onto the scene and started offering Data Integration with mainframe computers and personal computers.
Extract Transform Load (ETL) tools quickly became the go-to solution for organizations trying to synergize data from multiple sources. With ETL, organizations are gradually moving from conventional data storage strategies to multi-cloud Business Intelligence opportunities.
Modern-day ETL tools allow organizations to simplify the aggregation of disparate sources without exhaustive coding processes. However, to ensure data quality and consistency across organizations, various ETL Testing practices have become a mandatory aspect of Data Warehousing.
In this article, you will learn about the best tips and practices for ETL Testers.
Table of Contents
What is ETL?
Image Source: Microsoft
ETL is a process to aggregate data into Data Warehouses for enabling organizations to analyze and drive business decisions.
- Extract: This process captures and integrates data in all forms from multiple databases, Data Lakes, and CRMs.
- Transform: This process forms the most critical part of an ETL pipeline and deals with converting data into analytics-ready by using techniques such as grouping, sorting, cleaning, and pivoting.
- Load: This process deals with loading structured and unstructured data from data lakes, databases, and other sources typically into Data Warehouses for Data Analysts to draw insights with a few clicks.
Today, ETL has become an essential part of an organization’s broader Data Integration strategy. As the name suggests, the ETL process consists of extracting data from a source system, transforming the data into a system-friendly format, and then loading it into a Data Warehouse. ETL is primarily used for building Data Pipelines by cleansing, profiling, validating, and auditing Big Data.
You can also check our article on Database ETL.
It provides deep historical context for businesses to leverage data residing in their data lakes and other databases. ETL makes it easier for organizations to analyze data and set up new initiatives for faster decision-making.
Modern-day ETL tools also enhance productivity as they codify and reuse processes that move data, exempting the professionals from the burden of coding every part repeatedly. This allows consistency and helps maintain accuracy with reporting and analytics while providing auditing typically required for Data Warehousing.
Learn in-depth information about ETL in the following guide – Ultimate ETL Guide: 14 Comprehensive Aspects. Know how ETL and OLAP Data Warehouses in this guide – ETL Data Warehouses: The Ultimate Guide.
What are the Challenges of ETL?
One of the biggest challenges in the ETL process is the low-quality data that reflects in the business insights. As a result, it augments poor decision-making, which can negatively impact your business processes.
This becomes a massive challenge, especially when Data Analysis is performed at an enterprise scale. To limit organizations from accessing inaccurate information, ETL Testers need to validate, verify, and qualify data, assisting in maintaining the health of the entire data operations.
What are the Work of ETL Testers?
The job of ETL Testers is to write test cases that can simulate the actual processes. The ETL Testers write SQL queries that can target specific ETL processes. Testing for the Data Extraction includes monitoring the data source systems and assessing how certain data contributes to Data Warehouses. And when it comes to loading, the ETL Testers load the data into staging environments with checkpoints before injecting it into Data Warehouses or Data Marts.
ETL Testers are also responsible for simulating the whole transformation logic. They run scripts simulating the transform process and check if the ETL system needs troubleshooting. Finally, the ETL Testers test the dashboard to ensure that the Data Analysts and Business Intelligence engineers are making most of the data at hand. Here are a few commonly used tests on datasets:
- Metadata Test
- Data Completeness Test
- Data Quality Test
- ETL Performance Test
- ETL Integration Test
Responsibilities of An ETL Tester
As an ETL Tester, on a broad level, your work will revolve around validating data sources, extracting data, and applying data transformation for further analysis. The specifics and level of control you have over these processes will vary depending on your role. Moreover, you can/likely encounter additional responsibilities like:
- Designing and developing UNIX commands as part of the ETL process and automating the process of extracting and loading the data.
- Working and writing SQL queries extensively using SQL, ANSI SQL, and PL/SQL and testing database functionality using different tests like data validation, regression testing, etc.
- Proficiency and experience while working with test case preparation and execution. Your knowledge of software test life cycles and test case life cycles would be put to practice in these scenarios.
- Using JIRA to track and report on defects, creating and collaborating on project roadmaps.
- Using data architects to test database schema.
- Creating and running automation scripts as part of regression testing.
- Prioritizing requirements based on company needs and submitting all requirements to QC.
- Testing the functioning of the program’s front end on a Java platform application.
- Assisting in the analysis of incoming equipment and the development of required control programs in Linux and Unix.
- Using reporting services, create a bug metrics report assessing the team’s overall productivity in TFS.
Best Practices for ETL Testers
The goal of ETL Testers is to ensure the following:
- Data Correctness
- Data Integrity
- Data Transformation
- Data Quality
- Ease of Scalability
ETL of the 1970s is quite different from what it is today as enterprises are migrating towards cloud environments, thereby bringing a new breed of data challenges.
Today, organizations are gradually moving towards unstructured data to gain insights from audio, video, and text data types. As a result, ETL has evolved over the years to support processing semi-structured data to enhance organizational capabilities with Business Intelligence and Data Analytics tools.
Here are a few best practices for the ETL Testers to keep the Data-Integration Pipeline intact:
- Robust Checkpoint Creation
- Checking for Scalability
- Finding the Right Tool
1. Robust Checkpoint Creation
An ETL Tester is tasked with scheduling, auditing, and monitoring ETL jobs to ensure that the loads are performed as per expectation. A good rule of thumb is to create a system of checkpoints at every stage of the data pipeline.
The ETL Testers should write tests that can validate the incoming data as well as transformed data. These tests should also verify if data has been mapped correctly during the transformation stage and evaluate whether it is following the enterprise’s objectives.
Effective test cases of ETL pipelines are critical as they can be reused for reducing downtime significantly while supporting analytics across different departments. A good ETL Tester never underestimates trivial alerts and is always on the lookout for anomalies. It is important to log everything and create informative alerts and notifications.
Most of the ETL issues can be solved by spotting the errors early on. Understanding the state of source data can stop the data quality issues from breaching the ETL pipeline later. ETL Testers should thoroughly inspect the source system and try to rectify them at the source system level itself.
2. Checking for Scalability
The size of data in the Data Warehouses can change with every ETL cycle, making it impossible to refresh them during every ETL cycle. If your organization has a pattern of data surges, as an ETL Tester, it is recommended to test the pipeline performance for incremental loads.
The lack of timestamps in the source systems can result in data capture inconsistencies. Enterprises can’t afford to troubleshoot at the last minute as ETL processes are usually time-consuming. Agility is the key, and it should be tested whether the data loading happens in a given timeframe. Consequently, ETL Testers must ensure that the performance is checked for incremental loads.
3. Finding the Right Tool
Last but not least is the selection of an appropriate ETL tool. The current landscape of Business Intelligence is under a deluge of fine tools. These tools have their advantages and disadvantages and can be assessed based on the use case at hand.
Depending on the requirements, tool compatibility should be evaluated based on various factors like support for data sources, user interface, levels of automation, and more. ETL Testers must also look at tools that can exhaustively validate data at all intermediate stages in the ETL process to ensure data completeness automatically.
Here are a few quick tips to make the best use of ETL Testing:
- Validate the ETL process by writing test cases with possible failures in mind.
- Disable all triggers in the destination table.
- Update test cases for reusability.
- Always test with the threshold of data volume higher than the current requirement.
- Enable recovery points to handle unexpected breakdowns.
1. Hevo Data
Image Source
Hevo Data is a no-code ETL tool that simplifies the ETL process as it supports 100+ data sources. It provides a simple interface for the ETL Testers to monitor all the activities that occur within pipelines. The platform can detect the schema of incoming data and replicate the same in the Data Warehouse without any manual intervention.
Get Started with Hevo for Free
Moreover, Hevo’s fault-tolerant architecture enables the secure handling of data. Manually coding ETL processes are complex, even for experienced developers. No-code ETL platforms like Hevo Data make ETL development easy and secure.
Check out some of the cool features of Hevo:
- Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
- Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the pipelines you set up. You need to edit the properties of the event object received in the transform method as a parameter to carry out the transformation. Hevo also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.
- Connectors: Hevo supports 100+ integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; and MySQL, MongoDB, TokuDB, DynamoDB, PostgreSQL databases to name a few.
- Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
- 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources like Google Analytics, that can help you scale your data infrastructure as required.
- 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
Sign up here for a 14-Day Free Trial!
With Hevo Data, you can start with free and then opt for a basic plan that starts at $249/month. You can explore the detailed pricing here.
2. Talend
Image Source
Talend Open Studio is an ETL tool that makes it easier to discover, federate, and share trusted data to automate data processes while enhancing data quality. The Talend Data Fabric platform is an industry-leading ETL tool for Data Integration, Testing, and Data Governance. Along with basic ETL Testing functionality, it supports continuous delivery mechanisms that run ETL Testing jobs on remote systems.
Talend Data Integration basic plan starts at $12,000/year. Read more about Talend pricing here.
3. Xplenty
Image Source
Xplenty is a popular Cloud ETL tool that offers an easy configuration to extract from or load data to multiple popular data sources — on the public cloud, private cloud, or on-premise infrastructure. It is a complete toolkit for the orchestration of Data Pipelines to streamline the data flow across different Business Intelligence tools.
Xplenty doesn’t disclose pricing. Xplenty offers a free 14-day trial to all new customers, and if one wants to proceed, the fees may vary based on the number of connectors. Read more about Xplenty pricing here.
Image Source
Informatica PowerCenter is an enterprise-grade Data Integration platform that is optimized for modern Cloud Data Warehouse patterns. Informatica has even recently announced the availability of the Cloud Data Integration Free Service, which fast tracks ETL Data Integration, Transformation, and Loading of priority workloads to Azure Data Services.
Informatica Power center cloud starts from 2000$ per month for its most basic version. You can get more information on Informatica Pricing here.
5. AWS Glue
Image Source
Amazon’s AWS Glue is a serverless Data Integration service that marries ETL with Data Analytics, Machine Learning, and Application Development. With AWS Glue, one can start analyzing data and deploy it within minutes. This tool provides both visual and code-based interfaces to make Data Integration easier. ETL Testers can visually create, run, and monitor ETL workflows with a few clicks in AWS Glue Studio.
AWS Glue follows a pay-as-you-go pricing model; it charges an hourly rate, billed by the second. But AWS Glue Data Catalog has monthly pricing. Read more about AWS Glue pricing here.
Conclusion
In this article, the significance of ETL was discussed and how the ETL tools can add value to enterprises. The article also focussed on the best practices that ETL Testers should imbibe to maintain the quality of data for aligning with business requirements.
Visit our Website to Explore Hevo
Extracting complex data from a diverse set of data sources can be a challenging task and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases or SaaS applications into your Data Warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code. You can have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
Want to take Hevo for a spin? Sign Up here for a 14-day free trial and experience the feature-rich Hevo suite first hand.