Achieving scalable solutions with big data analytics services

One of the most significant challenges faced by the client was their inability to scale their data storage and retrieval. We implemented the NoSQL concepts on the SQL server wherever required. This stores data in documents, instead of tables, which means the client can now store and process data at a scalable rate. We also used a reserved throughput model, which is based on the number of reads and writes data needs, rather than the space taken up in the CPU or memory, or even the IOPS. It natively partitions data for high availability and unprecedented scalability. Finally, we built all of this on a scale-out architecture that can handle large volumes of data at incredibly high speeds.

Accelerating the client’s big data analytics started with speeding up how the data was stored and accessed. First, we enabled the system to store multiple data structures: unstructured, semi-structured, or structured data were all supported. We also worked with the client to move their most frequently accessed data to SSD storage, which cut response times to just milliseconds. We even utilized a clustered index to make querying even quicker. All this was complemented by the reserve throughput model and scale-out architecture, which provided easier and more flexible access to data. Finally, our data analytics solutions were based on a relaxed consistency model, which has guaranteed our client data availability, throughput, low latency, and consistency on all single-region accounts and all multi-region accounts.

We are proud of what our data analytics solutions have provided to our client so far, but we know that the challenge of an increasingly complex, ever-changing market landscape will persist. If anything, it is likely to get even more complex. Therefore, we wanted to make sure the client’s engineers and IT team could change and adapt this data management system over time. We put in support for consistency levels like eventual, consistent prefix, session, and bounded staleness. This gives the system full flexibility and a low cost-to-performance ratio, giving the client time, space, and budget for any necessary changes. We also ensured that storing the datasets didn’t require complex joins, foreign keys, or stored procedures, bringing simplicity to the system that would make it easier to maintain and update. We also integrated easy updates to schemas and fields, so that internal teams and clients could update or change their system as they needed to, without raising a ticket and taking up valuable IT time.