In this blog post, we’ll look at the many ETL tools available today and how you can assess & use them to your advantage.
What are ETL tools?
ETL tools help you move data from one system to another. They are a convenient alternative to manually assembled & maintained extract, transform, load processes.
Organizations create and consume a lot of data from many sources, in different formats and at different speeds. What is difficult is capturing that data and transforming it into a consumable format that is accurate and actionable.
The best ETL tools solve this issue by:
- Gathering large volumes of raw data from many data sources
- Changing data into understandable formats
- Putting transformed data in repositories for specific business analytics or business intelligence uses
Why do you need ETL tools?
You can choose not to use any ETL tools and go for hand-coding instead. Hand-coding may seem like a cheaper & easier option, but as the amount of data increases and tasks become more complicated, you will find hand-coding is not workable or cost effective.
ETL tools are 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 help you to use drag-and-drop functions. This speeds up the process of mapping tables and columns between the source and target storage.
- Advanced processing:
Tracking data changes automatically, you can copy changed data without needing 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 are ready to use.
- Greater control:
Support for transformation scheduling, version control, monitoring and unified metadata management.
- Operational resilience:
Automation of several parts of the integration process. This reduces 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 changed, different types of ETL solutions have entered the market. Some are made to work in an on-premises data environment. Some are made to work in the cloud. And some are made to work in hybrid environments. Today many options are available for different budgets and needs. Here’s a 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 cost-effective way to do ETL since they use limited resources for a limited time.
- Real-time ETL tools:
Today, most organizations need real-time access to data from different sources. Let’s say your customers are looking at your website for products. You need to be able to suggest relevant products as soon as possible. Use cases like this have caused a shift to real-time ETL tools. Apache Kafka is the leading platform for ingesting streaming data in real time. 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 that is free. This helps 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 in quality, integrations and ease of use. They also vary in adoption and availability of support.
- Cloud-native ETL tools:
With more 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.
Identifying the right ETL tool for you: 5 key factors to consider
There are many ETL tools to choose from. So, how do you know what’s best for your business? It comes down to your business needs. Here are some of the key factors to consider when you’re reviewing ETL tools:
- Use case:
Your use case should be one of the most critical factors in your decision. If you don’t think you need real-time updates you can go for an existing batch tool. But if you are planning to move to the cloud, then cloud-native tools would be more helpful.
As your business grows, so will your ETL processing needs. This includes volume of data, diverse data sources and formats. It also includes processing steps, concurrent data loads, third-party invocations, etc. Look for an ETL tool that will help with future business needs.
- Error handling:
Sometimes unexpected 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. This will ensure 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 changing business needs.
- Cost optimization:
Cost is one of the most important factors for any buying decision. Look for ETL tools that can also deliver ELT. This reduces costs by transforming the data inside the data store by using the resources in the repository.
Best practices: How to make the most of your ETL tools
Every step in the ETL process is important. By creating a set of ETL best practices, you can make each step – and the process – more consistent. Here are the 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 a lot of information on hand for analysis, too much data flowing through your ETL pipelines can slow things down. Modern ETL tools with change data capture capability capture changes across many environments as they occur. This helps your IT organization to deliver up-to-the-minute data to the business.
- Maximize data quality:
Make sure your data is reliable by feeding clean data into your ETL processes. Intelligent data quality tools can help you gain meaningful business value.
- Leverage AI and automation:
Use automation to make your ETL integration processes efficient. ETL integration automation means reducing the role of human operators. Some ETL tools let you integrate cloud and on-premises applications with no coding required.
- Manage mappings:
Manage mappings like how you handle source schema changes. There are data types, security permissions, and naming conventions to keep in mind. 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:
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 creating your ETL architecture, make sure to rank what information to include in your logs.
- Implement auditing:
Establish, collect and analyze metrics that provide visibility into your ETL processes. A well-made process will check for errors and support auditing of row counts, amounts and other metrics.
- Set up alerts:
Sending alerts as soon as there are errors helps with solving them immediately. There are tools that deliver preview support for your workload migration for early error detection.
Monitoring the health of your ETL processes in real time helps identify performance issues before they can impact your cluster.
Customers choose Informatica for their ETL needs
Most data-driven organizations are choosing 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:
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 not built for standardization. They were looking for a cloud-first integration platform that could address their existing challenges and help as they grow and scale.
They chose Informatica because of its integration capabilities. They also chose Informatica because it supports Snowflake and Azure, which was a specific requirement for Home Point. They were able to achieve cost savings by using a platform that does not need a large investment in human resources.
- To speed up the pace of customer service delivery, Grant Thornton wanted a trusted data integration provider to help its clients access and manage data. They also needed a partner to help 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%. It also improved business decision-making, financial analytics and customer service with faster access to data.
- For years, JDRF has used Salesforce to manage donor information, collecting approximately 10 million donor records. Over time, the data became duplicative and was often incomplete. JDRF had to use outside vendors to clean the data, which was costly. They also asked employees to cross-check and compare multiple donor records in Salesforce, which was time-consuming.
To help make more data-driven decisions, JDRF needed to establish a single 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 donor base to 3 million records. This provided a clear and complete picture of its supporters.
JDRF uses Informatica Data Management Cloud 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%. This helped them focus their 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 make sure that the underlying data moves across the clouds, regardless of where it lives (on-premises, public or private cloud). This requires a cloud native ETL and ELT with ecosystem support.
Informatica offers AI-powered cloud-native data integration that helps you to create data pipelines across a multi-cloud environment consisting of AWS, Azure, Google Cloud Platform, Snowflake, Databricks, etc.
- 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 ETL and ELT free for 30 days.