![]() ![]() Want to build an analytics dashboard that’s regularly updated? ETL makes that possible too. Need to move all your CRM data into a cloud data warehouse? ETL handles that at any scale. ETL provides a way to prevent data silos and brings raw data from many different sources together into a cloud data warehouse so a business can use it for analysis, decision-making, and product development.ĮTL is most often used by enterprise DataOps teams to manage data analytics operations, but small businesses, startups, and entrepreneurs can use it too. Without efficient ETL tools to move data from many sources into a central location, businesses end up with data silos and incompatible data sets that can’t be used across the company. Otherwise, you could end up with bad data, conflicting analytics, or potential security risks.The extract, transform, and load process (ETL) is critical for any organization handling large volumes of data. A staging or landing area for data currently being processed should not be accessible by data consumers. Often, the use of interim staging tables can improve the performance and reduce the complexity of ETL processes. Embedding email notifications directly in ETL processes adds unnecessary complexity and potential failure points. In this post, I share some of the design patterns for handling bad data. When suspect data is discovered, there needs to be a system for cleansing or otherwise managing nonconforming rows of data. What happens when things go wrong? This post reviews design patterns around prevention and management of errors in ETL processes. How big should each ETL process be? In this post, I discuss the merits of properly sizing your ETL logic.Įrror Handling. ETL modularization helps avoid writing the same difficult code over and over, and reduces the total effort required to maintain the ETL architecture.ĮTL Atomicity. Creating reusable code structures is important in most development realms, and even more so in ETL processes. Understanding where data originated from, when it was loaded, and how it was transformed is essential for the integrity of the downstream data and the process that moves it there.ĮTL Modularity. A well-designed process will not only check for errors but also support auditing of row counts, financial amounts, and other metrics.ĭata Lineage. ![]() A load without errors is not necessarily a successful load. In this post, I share some of the essential concepts around logging ETL operations.Īuditing. Logging: A proper logging strategy is key to the success of any ETL architecture. Speed up your load processes and improve their accuracy by only loading what is new or changed. The What, Why, When, and How of Incremental Loads. What is ETL? For those new to ETL, this brief post is the first stop on the journey to best practices. In the coming weeks and months, I’ll be blogging about each of these in detail. However, for most ETL processes, the best practices detailed below should be considered central to the architecture.īelow I’ve listed some of the essentials that are key to most any ETL implementation. Even for concepts that seem fundamental to the process (such as logging), there will certainly be edge cases that negate the need for one or more of these. I’m careful not to designate these best practices as hard-and-fast rules. So whether you’re using SSIS, Informatica, Talend, good old-fashioned T-SQL, or some other tool, these patterns of ETL best practices will still apply. However, the design patterns below are applicable to processes run on any architecture using most any ETL tool. Most of the examples I flesh out are shown using SQL Server Integration Services. Following these best practices will result in load processes with the following characteristics: Over the course of 10+ years I’ve spent moving and transforming data, I’ve found a score of general ETL best practices that fit well for most every load scenario. Establishing a set of ETL best practices will make these processes more robust and consistent. Each step the in the ETL process – getting data from various sources, reshaping it, applying business rules, loading to the appropriate destinations, and validating the results – is an essential cog in the machinery of keeping the right data flowing. Extract, Transform, and Load (ETL) processes are the centerpieces in every organization’s data management strategy. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |