As a data integration expert, ETL can be your best friend—or your worst nightmare. When implemented correctly, ETL performs the heavy lifting for your data integration workflows, efficiently collecting and centralizing vast quantities of information from a wide variety of sources.

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

When things go wrong, however, ETL can be the source of serious pain points and bottlenecks that prevent you from gaining valuable data-driven insights. The good news is that you can largely avoid these pitfalls by following best practices when designing and building ETL architecture. 

Our previous article gave a general overview of ETL architecture, including the various steps involved in implementing an ETL workflow. Now, we’ll cover these issues in greater detail, along with several best practices for ETL architecture and a few of the most common challenges you’re likely to encounter during your build.

Table of Contents

  1. Four Best Practices for ETL Architecture
  2. Three Challenges to Consider
  3. One Solution for Better ETL

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Four Best Practices for ETL Architecture

1. 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'll actually need. While it's good to have lots of information at hand for querying and analysis, too much data flowing through your ETL pipelines can slow things down considerably.

But how can you efficiently extract the data you need for BI and analytics workloads—and no more? Data profiling and cleansing will be essential here in order to remove duplicate and unnecessary information. In some cases, the right answer may also be an ELT (extract, load, transform) architecture. With ELT, your data is loaded into the data warehouse before being transformed as necessary. ELT allows you to do ad-hoc transformations when you run a particular analysis, which is much faster than transforming all of the data before it enters the data warehouse.

2. Optimize Your ETL Workflow

Because ETL deals with such massive quantities of data, even relatively minor tweaks can have a major effect on the final performance of your 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

3. Use Logging and Monitoring

Using the 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. This may include:

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

4. Choose the Right ETL Tool for You

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

Choosing the right ETL tool can only be done when you already have an ETL architecture in mind. You should have a clear idea of which data sources and targets will be involved, 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.

Three Challenges When Building an ETL Architecture

1. Performance

"ETL" and "high performance" are two concepts that don't always go together, especially when it comes to 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.

2. Data Security and Privacy

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

Your ETL architecture must carefully preserve the security and confidentiality of sensitive enterprise data at every step. For example, ETL platforms like Xplenty 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.

3. 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 many organizations' 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 when you need to change sources and rearrange certain elements of your ETL architecture.

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One Solution for Better ETL

Putting these ETL best practices into action while dealing with these substantial ETL challenges is tricky for even experienced developers. That's why more and more organizations opt for low-code ETL data integration platforms like Xplenty.

Xplenty helps you comply with all the ETL best practices that we've outlined here:

In addition, Xplenty 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 straightforward drag-and-drop interface with more than 100 pre-built integrations, letting you easily make changes to your ETL workflows

Xplenty also claims the title of "Winter 2020 Leader" in cloud data integration and has received a number of rave user reviews.

Want to try Xplenty’s powerful data integration features out for yourself? Get in touch with our team for a chat about your business and data requirements.