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Business News/ Brand Stories / Sigmoid delivers increased data accessibility & accelerates insights with cloud

Sigmoid delivers increased data accessibility & accelerates insights with cloud

Cloud data warehouses enable seamless access to data, improved query performance, better scalability with lower TCO. Towards this, Sigmoid helps large enterprises modernise their data foundation on the Cloud with their end-to-end data engineering processes

Sigmoid helps large enterprises implement cloud data warehouses, migrate data, and build robust data pipelines to improve performance and lower the TCO.Premium
Sigmoid helps large enterprises implement cloud data warehouses, migrate data, and build robust data pipelines to improve performance and lower the TCO.

What started as a technological curiosity about two decades ago has become  a core element of the IT landscape of all companies, big and small, who are shifting to Cloud to accelerate their digital transformation. In fact, by 2025, more than 95 per cent of new digital workloads will be deployed on cloud-native platforms, according to Gartner. 

Most companies today are ready to create value with the Cloud and leverage the capabilities that a cloud-based platform offers as against a conventional on-premise system, right from lower operating costs to greater flexibility and compatibility. As more people need access to real-time data, Cloud has reduced the dependence on IT teams and made employees even without a tech background more data savvy. 

Lokesh Anand, CEO and Co-Founder, Sigmoid said “Sigmoid is helping enterprises across industry verticals and functional areas harness the power of cloud for data analytics to drive business outcomes, agility and innovation. Our cloud data warehouse implementation and transformation services coupled with DataOps enables organisations to build data pipelines on the cloud at upto 40% lower total cost of ownership as compared to on-premise data warehouses".

Big Data Changed Data Warehousing

Big data is about innovation — dramatically altering how data is stored and managed. These innovations put tremendous pressure on traditional data warehouse processing. The three V's of big data (velocity, volume, and variety) pushed the traditional ETL's capability, resources, cost, and timeliness to the limit. 

Big data completely transformed the data warehouse landscape with the introduction of data lakes and flexible storage schemas. A well-known, predefined schema was no longer required for data lakes. The transform component of ETL was fundamentally changed by new data sources, data formats, and varied data validation requirements. Big data has also transformed how data consumers use the data warehouse, emphasising the importance of providing immediate access to business data rather than waiting for IT to curate it before making it available. 

“In the last few years we have been observing that the complexity and cost of handling data has increased for almost all large organisations and this is where leveraging cloud data infrastructure becomes very important. We have established methodologies to streamline the data migration journey and optimise the cloud costs while improving the performance of analytics workloads", said Mayur Rustagi, CTO and Co-Founder, Sigmoid.

Data scientists use the cloud to ensure that data is trusted, sensitive data is masked and data engineering pipelines are operationalised. This allows them to spend less time on data preparation and channelise their efforts towards solving business problems.                                                      

Democratisation of data

Today, enterprises have high volumes of data flowing in from multiple sources, which needs to be cleaned  and transformed. As business expectations for on-demand data exploded, data warehousing teams increasingly shifted their data storage efforts to the cloud. The need for business users to seamlessly harvest data from diverse sources and presenting it in an easily consumable format to decision-makers has made cloud data migration and warehousing indispensable today. 

Cloud and data engineering has created a new breed of data citizens, who can access data and process it for analysis, without having to rely on IT experts. This is possible due to the strong data governance mechanisms that are available today.                          

Sigmoid as an enabler for Cloud transformations

Sigmoid helps large enterprises implement cloud data warehouses, migrate data, and build robust data pipelines to improve ML model performance and lower the total cost of ownership. Their capabilities in the end-to-end data engineering process, stands out from the traditional software engineering approach that is largely adopted by in-house teams and system integrators. Sigmoid’s partnerships with cloud platforms have helped in building robust solutions on multi-cloud environments that allow businesses to get the best blend of all platforms. 

Sigmoid helps large enterprises implement cloud data warehouses, migrate data, and build robust data pipelines to improve performance and lower the TCO.

  • A programmatic advertising technology company embarked on their cloud transformation journey by migrating their on prem infrastructure to the Cloud. They saved over $ 2.5 million annually by selecting the right cloud platform and also improved the speed of data availability by 15X. 
  • A leading CPG company that operates Google Cloud storage and Databricks was able to make their ML model runtime faster by 13X and lowered cost per run by 87% with Sigmoid’s MLOps solutions.

With a plethora of options in the market, having a meticulous approach to aligning specific cloud capabilities with business objectives is very essential. Our expertise with all the leading cloud service providers has been instrumental in helping organisations evaluate and benchmark the right cloud vendor that would best suit their business needs", said Rustagi. 


Gartner reports that by 2023, 75% of all databases will be on a cloud platform. Extreme economic conditions and the pace of change have forced organisations to adopt a "cloud now" strategy for data and analytics. Moving data to the cloud is a significant step for businesses to seamlessly harvest data for faster decision making. However optimising the cloud infrastructure is a continuous improvement endeavour that is essential to keep up with the fast pace of data driven innovation.

Disclaimer: This article has been produced on behalf of the brand by the HT Brand Studio team.

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Updated: 23 Jun 2022, 11:19 AM IST
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