ETL is a data integration professional's best pal — or worst enemy. When implemented correctly, ETL does the heavy lifting for your data integration workflows, collecting and centralizing mind-boggling quantities of information from an incredible range of sources.

Related Reading: What is ETL? An Introduction to Data Integration.

When things go wrong, though, ETL can be the source of some serious pain points that prevent the data-driven insights you deserve. The good news is you can avoid these pitfalls by following best practices for designing and building ETL architecture. 

Last year, gave a general overview of ETL architecture, including the various steps involved in implementing an ETL workflow. Now, learn about the latest data integration challenges facing organizations like yours in 2021, along with best practices for ETL architecture and issues you might encounter during your build.

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4 Best Practices for ETL Architecture in 2021

Extract Necessary Data Only

Whether you're doing ETL batch processing or real-time streaming, nearly all ETL pipelines extract and load more information than you probably need. While it's good to have lots of information at hand for querying and analysis, too much data flowing through ETL pipelines can slow things down considerably.

So how can you efficiently extract the data you need for business intelligence (BI) and analytics workloads — and no more? Data profiling and cleansing are essential to remove duplicate and unnecessary information. Also, ELT (extract, load, transform) architecture can help. With ELT, you load data into a data warehouse before transforming it into a format for analytics. ELT allows you to do ad hoc transformations when you run a particular analysis, which is much faster than transforming all the data before it enters the warehouse.

Optimize Your ETL Workflow

Because ETL deals with such massive quantities of big data, even relatively minor tweaks can have a major effect on the performance of an ETL workflow. The top tips for ETL optimization are:

  • Avoid the use of SELECT * and SELECT DISTINCT in SQL queries during the extraction phase
  • Reduce in-memory merges and joins as much as possible
  • Schedule ETL jobs to run overnight or outside peak hours to avoid potential conflicts with other sessions and processes

Use Logging and Monitoring

Using ETL logging and monitoring is like saving up for a rainy day. You don't always need to execute these functions, but you'll be very grateful when you do. When designing your ETL architecture, deciding what information to include in your logs should be one of your priorities. Consider:

  • The timing of each data extraction
  • The time for each extraction
  • The number of rows inserted, changed, or deleted during each extraction
  • The transcripts of any system or validation errors

Use Pre-Built Integrations for Seamless Pipeline Building

Not all organizations have large data engineering teams that can build complicated pipelines from scratch. The best ETL tools come with pre-built integrations that move data from a source (for example, a relational database) to a final destination (for example, a data warehouse) with little or no code. These integrations also cleanse and transform source data so it's ready for analytics before it gets to its destination.

While some organizations require more complex pipeline builds, pre-built data integrations that come 'out of the box' facilitate the smooth flow of data from one location to another and benefit small- and medium-sized companies with more limited ETL requirements. Incorporate a tool that has a wide range of pre-built integrations into your ETL architecture. Ideally, you want a platform that offers integrations for the most popular data pipelines in 2021, such as those that move data from Salesforce

Choose the Right ETL Tool for You

Building the perfect ETL workflow is tough, even for seasoned professionals. There's no shame in seeking assistance from an ETL tool, library, framework, or platform. However, before you buy one, make sure it's compatible with your source and target databases, the type of data you're working with, and your preferred programming language.

You can only choose the right ETL tool when you already have an ETL architecture in mind. You should have a clear idea of which data sources and targets you will use, and which business initiatives your data integration pipelines will support. Only then can you identify the ETL solution that matches your most important criteria: for example, ease of use, or superior monitoring and logging capabilities to help resolve performance issues faster.

Also, consider:

  • The total cost of the ETL tool and how much value it provides your business. Look for any hidden charges or contract-related fees.
  • The pricing model. Ideally, you want to pay for the number of data connectors you use because that typically works out cheaper than paying for data volume.
  • Customer service. No ETL tool is perfect, and you'll want to contact a professional engineer if something goes wrong when executing your ETL architecture.  

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Building ETL Architecture: 3 Challenges to Consider


"ETL" and "high performance" are two concepts that don't always go together, especially for batch processing. The 'transform' stage, in particular, is often a performance bottleneck that, if implemented inefficiently, can massively slow down your ETL pipelines.

Improving ETL performance should be one of your primary goals when designing an ETL architecture that will go the distance. One of the best things you can do to optimize your finite ETL resources is to use parallel and distributed processing (for example, with Apache Hadoop and MapReduce). Other ETL architecture solutions include loading data incrementally and partitioning large tables into smaller ones.

Data Security and Privacy

Much of the data in your ETL pipelines may be sensitive or contain confidential information. Regulations such as the EU's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) strictly govern how organizations handle and manage their consumer data. There are other regulations, such as HIPAA and Sarbanes-Oxley, which apply to specific industries such as healthcare and finance.

Adherence to data governance frameworks like those above is more than a box-ticking exercise. Failure to comply with GDPR when processing data from customers in the EU, for example, can result in expensive penalties that might total thousands of dollars. Can your organization afford to pay that? Compliance has never been more important than in 2021.

Your ETL architecture must carefully preserve the security and confidentiality of sensitive enterprise data at every step. For example, ETL platforms like come with a rich set of hash functions that mask data during the ETL process before loading it into a data warehouse. Hash functions are one-way and irreversible—even if an attacker breaches the data warehouse, there's no way to recover the original information.

Flexibility to Changing Business Requirements

ETL pipelines depend on stability and predictability: the knowledge that source and target databases will remain in the same locations, the same repeated set of transformations enacted on each new batch of data, and so on. However, this runs directly counter to the organizational need for flexibility in the face of a rapidly evolving business landscape.

As mentioned above, planning your ETL architecture can be a good way to gain more flexibility within the data integration process. You can also future-proof your ETL architecture by choosing an ETL platform with a variety of database integrations. Picking an adaptable ETL platform makes it easier to change sources and rearrange certain elements of your ETL architecture.

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How Simplifies Best Practices for ETL Architecture

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Putting the above best practices for ETL architecture into action while dealing with data integration challenges is tricky for even experienced developers. That's why more and more organizations opt for low-code ETL data integration platforms like helps you comply with all the ETL best practices outlined here:

In addition, helps you avoid the biggest ETL challenges:

  • Smart optimizations to improve ETL performance
  • Compliance with data privacy regulations such as GDPR, CCPA, and HIPAA. 
  • A vast range of pre-built data integrations. Move data from relational databases, CRMs, ERPs, SaaS apps, and more. 
  • A straightforward drag-and-drop interface with an enormous, letting you quickly change your ETL workflows.
  • A simple pricing model that charges you for the number of connectors you use and not the amount of data you consume.
  • World-class customer service. is an award-winning cloud data integration solution and has received several rave user reviews.

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