ETL stands for Extract-Transform-Load, three database functions that are combined into one tool to pull data out of one database and place it into another database. Around 10 yrs ago nearly all the data warehouse systems were custom built, but the need for ETL software has increased with time and quite a lot of developers now use ETL tools in place of custom built solutions. Does custom coded (SQL) a data warehouse satisfy the needs of today, or is an ETL tool a preferred choice?
Let’s talk about the benefits of ETL tools.
We at LV Digital now generally recommend using an ETL tool. However, a custom-built platform can still be a practical choice for your business, especially when you have all your requirements laid out to granular specifics. In this blog we will give you a summary of the seven most crucial benefits of ETL tools and dish out some guidance on how to make the right choice for your organisation.
Visual flow
One of the biggest advantage of an ETL tool is that it gives a visual flow of the system’s logic if it is a flow based solution. Every ETL tool will present these flows in a different layout, but anything visual compared to a plain SQL, stored procedures and system scripts is a pleasure to have in stressful times.
Structured system design
ETL tools are created for one specific problem of data integration: populating a data warehouse or integrating data from more than one sources, or even relocating the data. Keeping in mind the maintenance and scalability, ETL tools provide a metadata-driven structure to the developers which is a huge advantage for companies building their data warehouse in early stages.
Operational resilience
Most of the custom-built data warehouses we have looked at are bit delicate: they have many developing operational difficulties. ETL tools deliver functionality and values for operating and monitoring the system in live environments. It is surely likely to design and build a well coded and structured hand-coded ETL platform. Nonetheless, it’s easier for a data warehouse / business intelligence team to build on the existing features of an ETL tool to build a strong ETL system from scratch.
Data-lineage and impact analysis
It should be a matter of clicks to click on a number and see full analysis or a breakdown of the number like how it was calculated, where the data was stored in the data warehouse, how it was transformed, when the data was most recently refreshed, and from what source system(s) the numbers were extracted. Impact analysis is the opposite of lineage: we’d like to look at a table or column in the source system and recognize which ETL procedures, tables, cubes, and user reports might be exaggerated if a structural change is required. If the ETL standards are absent, that hand-coded systems could depend on ETL vendors to provide this functionality — though, unfortunately, just half of them have done that so far.
Big Data
Most of ETL tools are capable of merging structured data with unstructured data in a centralized mapping. They can also handle huge amounts of data, without storing it in data warehouses. Hadoop-connectors or similar interfaces to big data sources are provided by almost 40% of the ETL tools nowadays. And the support for Big Data is growing every day.