![]() Without automation, overlooking errors is simply a matter of time.īesides that, developers also need to have access to an environment with all the chain components that they might need to refer to when fixing errors. That's why it's better to automate this process as much as possible so that the developers don't have to check all code changes manually. In this case, it's very important that developers don't forget about any intermediate tests when checking their code for errors - otherwise, poor-quality code will make it to the production environment and cause problems that might be very difficult to fix. In order to verify that the processing logic is working correctly, it is strongly recommended to test the data processing component together with the whole component chain (data extraction, transportation, processing, and storage)as opposed to testing it alone. Why we recommend building ETL processes with CI/CDĮTL incorporates several very different components: the data source, the method of data transportation, the processing logic, and the data storage. It's easier to take a framework with embedded scalability, then supplement it by writing and debugging your own code that carries out data processing logic tailored to your requirements. In this case, creating an ETL process from scratch isn't reasonable. Processing large datasets requires a cluster that can run ETL processes in parallel with workloads adjusted to network, disk space and CPU capacities. This process is known as ETL - extract, transform, load.Ĭompanies usually perform ETL using specialized software that allows for scaling in order to accommodate a growing volume of data. To see and analyze your data, you have to fetch it from its source, process it, and put it in some data storage system first. ![]() ![]() Existing data analysis tools include business analytics platforms, machine learning tools, and AI-powered analytic tools. ![]() In any case, analyzing this data helps you see what's going on and make better decisions. This data might be clearly visible to decision makers or stay hidden from them. Any modern technology-based business generates an enormous amount of data on a daily basis. ![]()
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