Streaming ETL or real-time data transformation is the new buzzword. Traditionally, enterprises have been extracting and storing data and then analysing the bulk data. However, with the rise of IoT – specifically sensor data – and big data, companies can no longer afford to wait longer to get insights from data.
The first step towards real-time data analytics is real-time data transformation. The data stream needs to be transformed into an analysable format on a real-time basis so that data analytics can take place in real-time as well. At Aceso Analytics, we help you transform your data architecture, enabling your IT team to perform streaming ETL for real-time analytics.
In a typical batch processing scenario, data is collected or updated after every certain amount of interval. Even if the data is collected on a real time basis, the processing does not happen as and when the data arrives. This means that the analysis of data happens long after the data is generated.
In a real time analysis scenario, data arrives and gets processed on a real time basis. In other words, the usefulness of data depends on its arrival time. For example, if the bank receives transaction data well after the transaction took place, it will have a hard time maintaining the correct account balance of customers.
While real time data transformation solves a lot of problems, it is more complex than batch processing and requires specialised technologies.
Aceso Analytics addresses the challenges of analysing data in real-time with real-time data transformation and processing. Since companies have been using batch processing for a long time, there needs to be a cultural shift in how we look at data. We make sure that our clients get the necessary technical as well as cultural hand-holding to make streaming ETL possible.
Real-time data transformation itself has so many challenges –
We support an end-to-end real-time data analytics pipeline – from ingestion to analytics and even ML model creation. As part of this pipeline, we design and architect a real-time data transformation pipeline that takes real-time ingesting and transformation into account.
Data that needs to be analysed on a real-time basis is quite different from the one that is analysed in batch. The keyword here is ‘sub-second.’ Your real-time data analytics pipeline must be able to ingest data on a real-time basis. There can be multiple ways of achieving this – the most popular one is by using Kafka. However, at Aceso Analytics, we believe one size does not fit all. Sometimes you need to use CDC on top of Kafka to track changes in data.
We always say that messy data leads to messy analytics. Hence companies clean and prepare messy data and transform it into analysable formats. In the case of real-time analytics, this transformation needs to happen on a real-time basis. Once again, we choose platforms and tools for this on a case-by-case basis, depending on the requirement. For companies that already use Kafka for ingestion, Kafka Streams is the simplest way to process real-time data streams. For a more performance-intensive process where sub-second latency is a requirement, we design the stream processing pipeline keeping Flink in mind. Ofcourse, the choice of tools also differs based on what cloud platform you use.
Manipulating data, especially in the case of real-time processing, requires a lot of ‘figuring out.’ We at Aceso Analytics do this ‘figuring out’ on behalf of you. The first question that we ask is- does the data really need to be transformed or processed in real-time? Once this basic question is answered, we don’t just start designing a pipeline in a generalised way. There are nuances that we focus on. For example, sometimes, dropping late arriving data is okay. However, in cases of bank transactions, stock trading etc., we can’t ignore late data. That’s what you get when you make Aceso Analytics your big data analytics partner!
Learn more about how we can help you and have your questions cleared.