![]() Open source is great, but if all you need to do is bring data to a data warehouse and do some transformations in the data warehouse with industry standard security, monitoring and alerting systems, managed open source service are amazing.įor example if you want to move data from multiple different OLTP databases and some files in different locations into a data warehouse without affecting the application performance, you will have to work with complex CDC This is a tricky one, since usually engineers have very strong feelings about using open source systems. Managed open source service v open source spending less time worrying about the service management.the speed of getting data pipelines up and running.In most cases a simple data engineering platform with easy to use tools will be much more efficient in terms of When designing a data engineering platform, most engineers jump to very powerful tools, which may not be necessary and adds to the complexity of the design and maintenance. Since these systems allow for cheap storage and are able to perform a wide range of transformations and eliminates the need for a separate transformation system, they have become quite popular. With the advent of powerful data warehouses like snowflake, bigquery, redshift spectrum that allow separation of storage and execution, it has become economical to store data in the data warehouse and then transform them as required. scheduling - used by engineers to schedule the ETL runsĮTL is the traditional processing framework, involving extraction of data from source systems, doing some transformations(cleaning) the data and finally loading the data into a data warehouse.ĮLT is similar, but in a different order as the name suggests.Alerting - used to alert engineers/analysts in case of failures. ![]()
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