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There is so much talk about data that it’s almost become a cliché. It’s true that data is being generated at an ever-increasing rate. That increase brings challenges for storing and managing the data, accompanied by challenges in converting the information into insights and business value.
It’s a classic case of separating the wheat from the chaff. And there’s quite a bit of chaff. Up to 70% of all data collected and stored within a company will never get to the analytics stage. That means only 30% of the data you collect will actually provide value for your company.
Many companies are examining the use of GraphQL to rise to these challenges. So, how can you use GraphQL to get more data from the storage stage to the analytics stage so you can actually gain insights from that information?
ETL vs. APIs
One way businesses bring data from the collection stage to the analytics stage is through extract, transform and load (ETL) processes. ETL software pulls data from various sources and feeds it through a pipeline directly into a “data lake” or data warehouse. You can transfer the data in batches, or you can transfer data in real-time as it updates, which is called a “stream” of data. Then, various types of analytics software can sort the data and present it to your team members.
ETL is great for when you need to compare big datasets. For example, if you need a day-by-...