What happens when your data just keeps on growing and growing? What are the challenges Big Data companies face when dealing with data? Well, these come in many different forms, from scalability and availability to real time demands and structure. Nevertheless, how do Big Data companies deal with these challenges? ETL, otherwise known as – Extract, Transform and Load – is a common process in data warehousing. This process Extracts data from external sources, Transforms it to fit certain needs, and Loads it to the end target. CoolaData’s ETL is paramount to our success.
While “Extract, Transform and Load” sounds simple enough, it really depends on how efficient your ETL system is. There are ETL systems that use only one server and are pretty basic. This works well for companies which acquire a small amount of data and therefore the risk of the server collapsing is relatively small. But we’re taking BIG DATA, and so an ETL system that is strong, smart and scalable is a must. How does the ETL work at CoolaData?
In eight steps:
- Events are pulled from a message queue. The message queue acknowledges that the certain event was handled only after being notified that the processing of each event has ended successfully.
- Each event is streamlined (CSV/JSON) into a key-value map according to our customer’s mapping of event fields, and formatting transformations are performed.
- Then our sessionization process begins (learn more about it here).
- Each event is validated for consistency down the stream of processing.
- The event’s fields are enriched and transformed based on our customer’s mapping.
- Certain functions are performed based on the mapping of each field in the event.
- Fields marked as User Properties and Session Properties in the metadata mapping are accumulated per user and session, and are then updated back to the relevant event.
- Each event’s data is used to accumulate and aggregate information on the customer, user and session levels.
Recent tech developments in the ETL process have enabled improvements in the overall performance when dealing with large volumes of data. This is mainly due to the implementation of parallel processing. The ETL has become a key factor in every Big Data company and continues to evolve.