In this blog post, we’ll look at the various extract, transform and load (ETL) tools available in the market and how you can evaluate and deploy them to your advantage.
What is ETL?
ETL is a data integration process that involves:
- Extracting data (structured and/or unstructured) from different types of sources
- Transforming it into a structure more appropriate for reporting and analysis
- Loading it into a data store (e.g., a data warehouse, data lake)
What are ETL tools?
In simple terms, organizations traditionally use ETL tools to move data safely and reliably from one system to another.
Any organization today is both generating and consuming a wealth of data from various sources, in different formats and at different speeds. The challenge is to capture that data and transform it into a consumable format that is both accurate and actionable.
ETL tools solve this issue by gathering large volumes of raw data from multiple data sources, changing it into understandable formats, and putting the transformed data in repositories for specific business analytics uses.
Why do you need ETL tools?
You can choose not to use any ETL tools and go for hand-coding instead. Prima facie, the latter may seem like a cheaper, easier, and quicker option. However, as the amount of data increases and tasks become more complex, you will realize that hand-coding is neither scalable nor sustainable – operationally, economically, or otherwise.
On the other hand, ETL tools may seem costly to begin, but are much easier and faster to use than hand-coding in the long run. In fact, ETL tools are key to processing large volumes of raw data because they provide:
- Ease of use: Graphical interfaces enable you to use drag-and-drop functions, which speed up the process of mapping tables and columns between the source and target storages.
- Expedited processing: Detecting delta changes means you can copy only changed data – without having to perform a complete data refresh.
- Advanced data cleansing and profiling: ETL tools allow you to apply and maintain complex universal formatting standards and semantic consistency to all data sets.
- Off-the-shelf transformations: Functionality including filtering, reformatting, sorting, joining, merging, and aggregation is ready for your operational use.
- Greater control: Support for transformation scheduling, version control, monitoring, and unified metadata management.
- Operational resilience: Automation of several parts of the integration process, reducing manual intervention – and probability of error.
Types of ETL tools available today
ETL tools have been around for more than a few decades. As technology and data integration processes have evolved, different types of ETL solutions have entered the market. Some are designed to work in an on-premises data environment, some in the cloud, and some in hybrid environments. Today various offerings are available for different budgets and needs. Here’s a quick summary of the ETL tools available:
- Batch processing or legacy ETL tools: These tools extract, transform and load the data into the target data warehouse or data lake in batches of ETL jobs. Until recently, these batch processing tools were a popular, practical, and cost-effective way to do ETL since they use limited resources for a limited time.
- Real-time ETL tools: Today, most organizations require real-time access to data from different sources. Let’s say your customers are browsing your website for products. You need to be able to suggest relevant offerings in the moment, not a day or even an hour later. Use cases like this have resulted in a shift to real-time ETL tools. Apache Kafka has emerged as the leading platform for ingesting and processing streaming data in real time, and organizations are building modern ETL solutions on top of it, either as a SaaS platform or an on-premises solution.
- Open-source ETL tools: These ETL tools have source code freely available and therefore help organizations keep their costs low while providing similar functionalities as other ETL tools. Most open-source ETL tools were created as a modern management layer for scheduled workflows and batch processes. These tools vary significantly in quality, integrations, ease of use, adoption, and availability of support.
- Cloud-native ETL tools: With more enterprise businesses moving to the cloud, they need a way to extract, transform and load data from sources directly into a cloud data warehouse. Cloud-native ETL tools let organizations gain key cloud benefits –such as flexibility and agility – in the ETL process. in the ETL process.
Identifying the right ETL tool for you: 5 key factors to consider
As you can see, there are a plethora of ETL tools to choose from. So, how do you know what’s best for your business? Ultimately, it comes down to your business requirements. Here are some of the key factors to consider when you’re evaluating ETL tools:
- Use case: Your use case should be one of the most critical factors driving your ETL tool decision. If you don't think you need real-time updates you can go for an incumbent batch tool. But if you are planning to move to the cloud then cloud-native tools would be more beneficial.
- Scalability: As your business grows, so will your ETL processing needs – volume of data, diverse data sources and formats, processing steps, parallelized data loads, third-party invocations, etc. Look for an ETL tool that will accommodate future business requirements.
- Error handling: Sometimes unforeseen issues can break a pipeline. For example, corrupt data or network failures may produce an error condition. Your ETL tool should be capable of handling errors efficiently, ensuring data accuracy and consistency.
- Performance optimization: ETL processes deal with large amounts of data and manage workloads through many different data flows. As the volume of data grows, so does the execution time. Your ETL tool should have built-in optimization features like pushdown optimization to address evolving business needs.
- Cost optimization: Cost is undoubtedly one of the most critical factors for any buying decision. Look for ETL tools that can also deliver ELT, reducing costs by transforming the data inside the data store, using the resources of the repository.
Best practices: How to make the most of your ETL tools
From extracting data to transforming it to applying business rules to loading it and validating the results, every step in the ETL process is essential. By establishing a set of ETL best practices, you can make each step – and the overall process – more robust and consistent. Here are my top 5 tips for any ETL implementation:
- Extract only what you need – Speed up your load processes and improve their accuracy by only loading what is new or changed. While it’s good to have lots of information on hand for querying and analysis, too much data flowing through your ETL pipelines can slow things down considerably. Modern ELT tools with Change Data Capture capability captures changes in multiple environments as they occur, enabling your IT organization to deliver up-to-the-minute data to the business.
- Maximize data quality – Ensure your data is reliable and accurate by feeding clean data into your ETL processes. Intelligent Data Quality tools can help you gain tangible business value.
- Leverage AI and automation – Use automation to make your ETL integration processes fast and efficient. In practice, ETL integration automation means minimizing the role of human operators. Certain ETL tools let you integrate cloud and on-premises applications with no coding required.
- Manage mappings – Manage mappings similar to how you track source code changes.
- There are data types and security permissions to consider as well as naming conventions to implement. Certain ETL tools have built-in capabilities like dynamic mapping and schema drift to support changes in the data structures.
- Keep checks and balances in place with:
- Logging - Using ETL logging and monitoring is like saving up for a rainy day: You don’t always need them, but you’ll be very grateful for them when you do. When designing your ETL architecture, deciding what information to include in your logs should be one of your first priorities.
- Implement auditing – Establish, collect, and analyze metrics that provide visibility into your ETL processes. A well-designed process will not only check for errors, but also support auditing of row counts, amounts, and other metrics.
- Set up alerts – Sending alerts as soon as there is an error helps in rectifying them immediately. There are tools that deliver preview support for your workload migration for early detection of errors.
Customers choose Informatica for their ETL needs
According to a 2020 O’Reilly survey, more than 88% percent of organizations use cloud infrastructure in one form or another. And, 45% expect to migrate three-quarters or more of their apps to the cloud by the end of 2021.
It’s fair to say that considering this scenario, most data-driven organizations would want to opt for cloud-native ETL tools to future-proof their business. Let’s look at a few of the organizations that chose Informatica’s cloud-native ETL to jump-start their data integration journey:
- Home Point Financial was using legacy systems to support its mortgage lending business, which were neither built for scale nor standardization. They were looking for a cloud-first integration platform that could address their existing challenges and help them be future-proof.
They chose Informatica not just because of its integration capabilities, but also because it supports Snowflake and Azure, which was a specific requirement for Home Point. Home Point was able to achieve cost savings by utilizing a platform that does not require an enormous investment in human resources.
- To accelerate the pace of customer service delivery, Grant Thornton wanted a trusted data integration provider that it could partner with to help its clients access and manage data, balance business risk, and achieve their technology goals. They chose Informatica to bring together data from on-premises and cloud applications for more informed decision-making.
Informatica helped them reduce their data integration development and maintenance lifecycle by 50% and improve business decision-making, financial analytics, and client services with faster access to needed data.
- For years, JDRF has used Salesforce to manage donor information, amassing approximately 10 million donor records. However, over time, the data became duplicative and was often incomplete. JDRF either used outside vendors to clean the data, which was costly, or asked employees to cross-check and compare multiple donor records in Salesforce, which was time-consuming.
To enable more personalized relationship management and more strategic, data-driven decisions, JDRF needed to establish a single, unique view of every supporter in Salesforce. JDRF chose Informatica due to its end-to-end data management capabilities. With Informatica Data Quality, JDRF reduced its existing 10 million records to 3 million unique supporter records, giving it a clear and complete picture of its supporter base.
JDRF uses Informatica Intelligent Cloud Services to connect and integrate on-premises systems across multi-cloud platforms, including AWS, Azure, Salesforce, web, and mobile. Informatica helped JDRF improve productivity by up to 40%, helping focus more of the non-profit’s resources on fundraising, research, and advocacy.
ETL in a hybrid and multi-cloud world
According to the Flexera 2021 State of the Cloud Report:
- 92% of organizations have a multi-cloud strategy in place or underway.
- 82% of large enterprises have adopted a hybrid cloud infrastructure.
The new architecture needs to ensure that the underlying data is interoperable and moves seamlessly across the clouds, irrespective of where it resides (on-premises, public or private cloud). This requires a cloud-native ETL and ELT with robust ecosystem support.
Informatica offers comprehensive, codeless, AI-powered cloud-native data integration that empowers you to create data pipelines across a multi-cloud environment consisting of AWS, Azure, GCP, Snowflake, Databricks, etc.
Accelerate your ELT and ETL use cases with Informatica’s free cloud data integration on AWS and Azure. Our free cloud data integration solutions simplify data access, letting you start projects faster with important benefits:
- Code-free: Build data pipelines with automated data integration
- Enterprise-scale: Build and run complex integrations with high-performance data ingestion
- Connected: Metadata-aware connectors for the most common data pattern
A faster, more cost-effective cloud-native solution for your data integration needs
With AI-powered, cloud-native data integration, Informatica provides best-of-breed ETL, ELT and elastic Spark-based data processing for any cloud data integration need. Try our industry-leading cloud-native ETL and ELT free for 30 days.