Why We Built the Lumada DataOps Suite

Why is DataOps important? Without intelligent data operations (DataOps), there can be no digital innovation. Agile data environments improve business operations and enable new customer experiences and new business models. Our customers demonstrate every day the value of their data and how it is critical for digital transformation.

Data Egress Cost Analysis

Understand the impact of data transfer and egress costs across Microsoft Azure, Amazon Web Services and Google Cloud Platform. One of the questions most frequently asked by cloud-savvy, price-aware customers goes something like this: OK, so we like that your tool makes it easy to integrate our cloud database and storage in our centralized data warehouse, but I know our budget will be scrutinized for total cost of ownership (TCO), including our data egress costs.

Using AI to Detect Stock Market Abuse | Part 1 | Snowflake Inc

NASDAQ has been transforming its system — integrating AI to identify stock market abuse, decrease latency, and modernize customer portfolios. Michael O'Rourke, NASDAQ's SVP of Machine Intelligence, details how he's led this transformation and the role Snowflake plays in the organization. Rise of the Data Cloud is brought to you by Snowflake.

The Security Challenges of Data Warehousing in the Cloud

Many organizations struggle to meet growing and variable data warehouse demands. No matter how much they pad their annual IT budgets, there never seems to be enough capacity to cover unexpected business requests. This leads to resource restrictions for the various business units that use the platform. When business units are not well served by central IT, “shadow IT” emerges.

How Data Fabrics Power Industrial IoT

Unlike typical resources companies depend upon to thrive, the amount of data available to enterprises is not finite. With edge technology and smart devices, there is truly no limit to the quantity of useful data companies can and should be using to make better informed decisions. But many businesses unnecessarily limit the variety, quality, and extent of the data at their disposal by not having the right data architecture.