With the rise of big data, how data is ingested, stored/streamed, and analysed needs restructuring. Traditional architecture appears insufficient for big data. What organisations need today is a scalable data ingestion and processing environment that is flexible enough to work with both structured and unstructured data. What this boils down to is designing a Big Data Architecture that can handle heterogeneous data and produce data products faster. We at Aceso Analytics design and build end-to-end big data architecture based on your requirement enabling you to ride your enterprise data instead of being drowned in it.
Three things should be remembered while designing a Big Data architecture –
Keeping these three things in mind, a generalised big data architecture will look this –
As you can see, a generalised big data architecture involves –
The first part of big data architecture is, of course, the data source. There can be various data sources – from traditional RDBMS to log data and even images and audio.
Since big data comes in various formats and structures, it’s impossible to organise it immediately. You need to have a large store where you can dump the data for future processing. Today, most companies prefer data lakes for this purpose.
Big Data involved in ATMs, stock trading software, and real-time games needs to be processed in real-time. This is where the big data architecture can differ. Some streaming data are preprocessed on edge, and then the aggregated data is sent to the server. Sometimes, edge computing isn’t added to the mix.
Data stored in data lakes can’t be used for initial exploration due to its varied structure and format. Hence next in big data architecture design comes a data warehouse where we make sense of unstructured data by organising it in rows and columns (SQL) or by leveraging NoSQL.
In the last part of the big data architecture design, we leverage the clean data in our warehouse to come up with predictive analysis using BI tools or to create features (selected data) for ML models. Basically, the data is now ready for prod.
Big data architecture design is fairly new, and there is no set standard. We at Aceso Analytics design big data architecture based on the requirement, technical maturity of the client company and the tech stack that our client is comfortable with. Here are two scenarios –
Our Approach: A typical architecture for this purpose would be Kafka → Spark → HBase → Spark. However, the company can streamline this architecture if it uses Druid instead of Spark. It has an excellent late-event management process, timestamp management and other benefits that can help the company avoid the complexities Spark based ecosystem brings.
Our Approach: Azure Synapse analytics is our go-to choice for this. However, if the client is too worried about vendor lock-in, our big data architecture might change.
We at Aceso Analytics thoroughly analyse your requirement, tech stack and future road map before designing your big data architecture.
Big Data architecture involves a lot of ‘figuring out.’ Come, let’s design an architecture that mimics the ambition and vision of your company.
Learn more about how we can help you and have your questions cleared.