How Do Data Pipelines Fit Into Your Data Stack?

In this article, we'll discuss what a data stack is, how data pipelines fit into and optimize them, and explore pipeline solutions.

The amount of big data generated around the world by the time you finish this page is limitless. Think about it for a second. Companies everywhere will create an innumerable amount of data right now—customer records, sales orders, chain reports, emails, you name it.

Companies need all this data for data analytics—the science of modeling raw data to uncover precious real-time insights about their business. It's like opening a treasure trove. But there's a problem: Most companies keep data in lots and lots of different places. The average organization draws from over 400 data sources, while 20 percent of organizations have more than 1,000 data sources. And that's a lot. 

Some of these data sources are new, and some are old. But because there are so many of them, data analytics becomes rather tricky. What if we could take data from all of these sources and move it to one place for analytics? Doesn't that sound like a much better idea? 

Extract, Transform, Load (ETL) does that.It's the most exciting thing to happen to data analytics in decades. 

In the simplest of terms, ETL:

• Extracts data from multiple locations.
• Transforms it into usable formats, and 
• Loads data into a data store like a data warehouse or data lake.

big data data science data integration etl data warehouse

In this article, learn more about ETLT and see an overview of ETL and ELT, advantages and use cases, and more. Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data Quality Testing Skills Needed For Data Integration Projects. Data integration projects fail for many reasons. Risks can be mitigated when well-trained testers deliver support. Here are some recommended testing skills.

In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

In this article, we'll discuss 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.

A data lake is totally different from a data warehouse in terms of structure and function. Here is a truly quick explanation of "Data Lake vs Data Warehouse".