Getting your ducks in a row is easier than managing the sprawling datasets that organizations have to juggle daily. Uncovering insights into business performance, determining the right growth strategy, and understanding where the next opportunity for innovation resides all depend on having quality data available from a variety of sources. Extract, transform, and load (ETL) tools are an essential cog in the wheel of modern data pipelines.
ETL tools are available in a range of different flavors and implementations, each with its own benefits and drawbacks. Most organizations today depend on a variety of business applications to manage operations including HR, CRM, ERP, and discipline-specific systems. Bringing these datasets together to form a holistic view of the business without ETL is an impossible task. Throw in the cloud era and traditional ETL approaches are no longer up to the challenge either.
To help you navigate the muddy waters that big data and decentralized operations bring to modern organizations, let’s look at the ETL tools and approaches you cannot do without in 2022.
- ETL tools have come a long way since running custom queries and scripts manually for each application
- With modern integrations to third-party visualization and validation systems, today’s ETL tool features enable large processing of decentralized datasets
- Enterprise and open-source ETL tools make it possible for any organization to ingest, validate, and analyze their data including automated data quality monitoring
Why New ETL Tools and Approaches are Necessary
The move from on-premise to cloud operations put traditional ETL tools and approaches under immense pressure. The painstaking process of adapting ETL rules to accommodate new data sources, formats, and relationships in today’s hyper-connected world cannot keep up when using outdated mapping techniques.
Some of the major concerns with traditional ETL tools are:
- Relies on custom-coded programs or scripts that become specialized within an organization
- Requires lengthy reengineering whenever the data model changes or a new application enters the technology stack
- Depends on a rigid process where data stewards need to sign off on every new integration and its ETL rules
- Cannot support continuous data streaming and transfers between systems automatically
How to Evaluate ETL Tools
Newer solutions can integrate with an organization’s data pipeline even in decentralized environments. When evaluating ETL tools, companies need to consider:
- The budget – Smaller organizations may have to skimp on features to stay within budget
- The use cases – Establish exactly how the process will look before analyzing the features of the tool
- Interoperability – Some ETL tools will only work with certain data sources and repositories while others are vendor-agnostic
- The UX/UI – No or low code systems are easier to deploy, maintain, and adapt as the data pipeline expands
Additionally, there is a shift from ETL to ELT (extract, load, transform) where the transformation process takes place in the target system after ingestion. This approach also has dedicated solutions that may be a better fit for the organization’s operational structure.
5 ETL Tools to Consider in 2022
Choosing the best ETL or ELT tool will require careful consideration. Below, we look at five of the best solutions available today.
1. IBM DataStage
Built for performance, IBM DataStage uses a client/server design with cloud and on-premise deployments available. Users can create and execute tasks against a central repository with data integrations for multiple sources. You can try IBM DataStage with a free trial before deciding which plan you want to take.
Hevo is a Fully Automated, No-code Data Pipeline Platform that supports 150+ ready-to-use integrations across Databases, SaaS Applications, Cloud Storage, SDKs, and Streaming Services. Try Hevo’s 14-day free trial today and get your fully managed data pipelines up and running in just a few minutes.
3. Informatica PowerCenter
Although the UI seems simple in the mapping designer, Informatica PowerCenter is also powerful enough to support the entire enterprise’s ETL processes. It can parse many data formats including JSON, PDF, and XML as well as machine (IoT) data. It’s easy to use and can scale with your business needs when adding more data pipelines without compromising performance. Similar to IBM DataStage, there is a free trial and paid plans available.
4. SAS Data Management
Designed to connect with all data across the data stack including data lakes, cloud, and legacy systems, SAS Data Management empowers non-IT stakeholders to build their own ETL workflows. It has integrations available for third-party modeling tools to help users analyze data using a holistic view of the organization’s business processes. Pricing is only available on request but you can try it before buying.
5. Talend Open Studio
Supported by an open-source community, Talend Open Studio has a drag-and-drop UI to pull jobs from sources like Excel, Oracle, Dropbox, MS Dynamics, and more. As it’s a free solution with built-in connectors to multiple environments, the solution can help organizations quickly set up their data pipelines and start analyzing information from relational databases, SaaS platforms, and other applications.
For more experienced and technically minded data professionals, Hadoop provides a framework for processing large datasets across a cluster of computers. Different modules provide a range of features including job scheduling, parallel processing, and a distributed file system to extract application data from any application. Hadoop is free and has a variety of related projects such as MapReduce related to the Apache software library.
Automated Data Quality Monitoring with Your ETL Tools
Apart from ETL tools, you’ll also need an automated data quality monitoring (DQM) solution like DataBuck and a data rule discovery system to ensure you can maintain the required data quality across your environment. By combining these tools with your ETL solution and integrating them with your data pipelines, you can improve the trust in your data while empowering your resources to make decisions confidently.
Embedded Video Recommendation: https://www.youtube.com/watch?v=2hMtdcF3ZAw
DataBuck automates your data quality validation tasks, speeding up the time to market when adding new data pipelines to your BI and analytical toolsets. SMEs can understand the data quality rules while data stewards can understand where their ETL processes may be failing. With cognitive data quality validation, you can quickly improve your analytical models using AI and ML automation from DataBuck.
If you want to see how DataBuck can help improve your ETL tools and processes, schedule a demo today.
Check out these articles on Data Trustability, Observability, and Data Quality.
- 6 Key Data Quality Metrics You Should Be Tracking (https://firsteigen.com/blog/6-key-data-quality-metrics-you-should-be-tracking/)
- How to Scale Your Data Quality Operations with AI and ML (https://firsteigen.com/blog/how-to-scale-your-data-quality-operations-with-ai-and-ml/)
- 12 Things You Can Do to Improve Data Quality (https://firsteigen.com/blog/12-things-you-can-do-to-improve-data-quality/)
- How to Ensure Data Integrity During Cloud Migrations (https://firsteigen.com/blog/how-to-ensure-data-integrity-during-cloud-migrations/)