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Data
Enterprise IT Asset Disposition

Achieving scalable solutions with big data analytics services

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Challenges

The world of IT asset disposition and e-waste recycling increased with technological advancements, making it difficult for the client to keep up.

Limits on scalability
Increasing volumes of data
Data security needs
Speed of accessing data
  • Devices and data disposal was becoming more complex
  • New regulations and more complex IT structures meant more data was being generated
  • Data was not conveniently organized or accessible
  • Requests for data often ended up with IT tickets being raised internally
Our Solution

While this client’s data was as big as it gets, their system wasn’t. They needed big data analytics services that could organize the data. Making it quick, easy, and secure to access, both by internal teams and by their customers. Our data analytics solutions focused on speed, scalability, and futureproofing.

Solution Impact

80%

Reduction

In the reports count

99.99%

Readability

On all multi-region database accounts

3

Data structures supported

Unstructured, semi-structured, and structured

Our Approach

Big data analytics solutions like this are complex and technical. We implemented NoSQL concepts on an SQL server, and we provided support for consistency levels for full flexibility and a low cost-to-performance ratio. We used a reserved throughput model and SSD-backed storage for high availability, scalability, and millisecond response times.

Our Approach
Baking horizontal scaling into big data analytics solutions

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.

Baking horizontal scaling into big data analytics solutions
Turbocharging the storing and accessing of big data

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.

Turbocharging the storing and accessing of big data
Anticipating the future of big data analytics today

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.

Anticipating the future of big data analytics today
Business Impact

Our big data analytics services accelerated our client’s data storage and access times to improve their data analytics services. With increased scalability and several future-proofing features, they can easily respond to their customers’ ever-evolving needs, both today and tomorrow. In short, we truly tamed our client’s data beast.

Responsiveness

Data response times reduced to milliseconds

Flexibility

TBs of structured data, semi-structured and unstructured data stored conveniently and logically

No downtime

A developer-friendly format that takes full advantage of the cloud has reduced downtime to 0

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02

Our experts set up a meeting to understand your requirements

03

We estimate and propose project efforts and timelines

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