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Top 7 ETL Tools for 2021

Organizations of all sizes and industries now have access to ever-increasing amounts of data, far too vast for any human to comprehend. All this information is practically useless without a way to efficiently process and analyze it, revealing the valuable data-driven insights hidden within the noise.

The ETL (extract, transform, load) process is the most popular method of collecting data from multiple sources and loading it into a centralized data warehouse. During the ETL process, information is first extracted from a source such as a database, file, or spreadsheet, then transformed to comply with the data warehouse’s standards, and finally loaded into the data warehouse.

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5 Customer Data Integration Best Practices

For the last few years, you have heard the terms "data integration" and "data management" dozens of times. Your business may already invest in these practices, but are you benefitting from this data gathering? 

Too often, companies hire specialists, collect data from many sources and analyze it for no clear purpose. And without a clear purpose, all your efforts are in vain. You can take in more customer information than all your competitors and still fail to make practical use of it.  

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How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges. 

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.  

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What Is Chaos Engineering?

In the past, software systems ran in highly controlled environments on-premise and managed by an army of sysadmins. Today, migration to the cloud is relentless; the stage has completely shifted. Systems are no longer monolithic and localized; they depend on many globalized uncoupled systems working in unison, often in the form of ethereal microservices.

It is no surprise that Site Reliability Engineers have risen to prominence in the last decade. Modern IT infrastructure requires robust systems thinking and reliability engineering to keep the show on the road. Downtime is not an option. A 2020 ITIC Cost of Downtime survey indicated that 98% of organizations said that a single hour of downtime costs more than $150,000. 88% showed that 60 minutes of downtime costs their business more than $300,000. And 40% of enterprises reported that one hour of downtime costs their organizations $1 million to more than $5 million.

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What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility. 
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.