ETL Processing and Best Practices

ETL Processing and Best Practices

ETL may be one of the most relevant if not the most relevant data integration approach (extract-transfer-load) that’s a crucial a part of the information and data engineering process.

Extract, Transform, and Load (ETL) processes are the real pivot in every organization’s data management strategy. Each step the within the ETL process – getting data from various sources, reshaping it, applying business rules, loading to the right destination, and validating the results – is an essential cog within the machinery of keeping the proper data flowing. Establishing a set of ETL best practices will make these processes more robust and consistent.

In general, ETL covers the process of how the data are loaded from a source system into a data warehouse. This operation is critical for data products, software applications, and all the analytics / data science & AI work. In most organizations, this process includes a cleaning step which ensures that the highest quality data is maintained and inconsistencies do not affect downstream work (i.e. BI and reporting)

Therefore ETL and data processing in general are crucial in order to provide organization with reliable, solid and updated data. And also are fundamental to avoid the following questions from your stakeholders:

“Why do our data loads take so long to run?”

“Why can’t we get our reports out earlier?”

“I get to the office early and need to be able to see results by the time I get in!”

 

Follow the suggestions below and hopefully your clients won’t have to ask these very questions at any time.

 

  1. Work towards scale and incrementality – For efficiency, seek to load data incrementally: when a table or dataset is small, most developers are able to extract the entire dataset in one piece and write that data set to a single destination using a single operation. Also keep scalability as one of the goals right from the start while breaking the ETL process into as many independent sub-modules as possible. If one module has some significant dependency, make sure you code for all the posible scenarios in dependent module. However, as the data sets naturally grow in size and complexity, the ability to do this reduces. Moreover, with data coming from multiple locations at different times, incremental data execution is often the only alternative. This is the main reason why incremental load is necessary in most cases.
  1. Focus on the idempotency constraint. As for the previous point, always try to optimize processes at the design level, rather than the actual implementation. This is important, as it means that, if a process runs multiple times with the same parameters on different days, times, or under different conditions, the outcome remains the same. One should not end up with multiple copies of the same data within ones environment, assuming that the process has never been modified. If rules changes for whatever reason, the target data, they way it is structured, will be expected to be different. However always try to reduce dependencies as much as possible on code being used for other flows. There is nothing wrong with having different copies of the code for different flows even if at present they have same function definitions.
  2. Make sure that you can efficiently process historic data. This is a very common scenario. Data can be corrupted or lost and incremental load of historical may become necessary at some point. In many cases, one may need to go back in time and process historical at a date that is before the day or time of the initial code push. To ensure this, always make sure that you can efficiently run any ETL process against a variable start parameter, enabling a data process to back-fill data through to that historical start data irrespective of the initial date or time of the most code push. To enable this, one must ensure that all processes are built efficiently, enabling historical data loads without manual coding or programming. This guarantees that data can be efficiently load with little delay for stakeholders, without affecting existing reporting or downstream BI capabilities.
  3. Store all metadata together in one place. This sounds almost like “build a datalake for all meta/unstructured data” and it is actually almost like that. Within good ETL, one should always seek to store all meta-data together. Once this is done, allow the system that you are running or workflow engine to manage logs, job duration, landing times, and other components together in a single location. This will allow one to reduce the amount of overhead that development teams face when needing to collect this metadata to solve analysis problems. Data ingestion involves getting data out of source systems and ingesting it into a knowledge lake. A data engineer would wish to understand the way to efficiently extract the info from a source, including multiple approaches for both batch and real-time extraction.
  4. Measure everything. Queries should be logged with the relevant timestamp and resources utilized. This is the only way you have to be truly effective at performance optimization. And also kill queries sucking up too many resources and call up users naively using them (and tell them why they should learn how to optimize their SQL).
  5.  Test before going to prod. Seems like common sense, but we all are guilty to reduce QA time for speedier development. It is always good to be better safe tan sorry.
  6. Investigate, understand and anticipate your client requirements and constantly ask questions. If applied correctly, this step is likely to save you most of the frustration in the implementation stage.

Anyone can build a slow performing system; the challenge is to create data pipelines that are both scalable and efficient. The ability and understanding of the way to optimize the performance of a private data pipeline and therefore the overall system are a well-sought engineering skill, but be ready to educate tech folks around you on how to optimize and use these tools correctly.