It’s no secret that the cloud data warehouse space is exploding. Driven by the need for on-demand, performant data warehousing solutions, businesses are turning to public cloud providers to modernize their analytics infrastructure and help them make better business decisions. Among the leading data warehouse options from the public cloud providers is Amazon Redshift. Redshift offers a petabyte-scale, fully managed data warehouse service in the cloud.

As a Microsoft partner, we’re excited by the announcement of the Azure Synapse Analytics platform. Why? Because it furthers the ability of businesses to leverage data-driven insights and decision making at all levels in an organization. (And we love that!) Together, our joint customers are already leveraging data in amazing ways to tackle everything from creating customer 360 views to reducing project times for data analytics from 6 months to 6 weeks.

While the impetus for transforming to a data-driven culture needs to come from the top of the organisation, all levels of the business should participate in learning new data skills. Assuring data availability and integrity must be a team sport in modern data-centric businesses, rather than being the responsibility of one individual or department. Everyone must buy in and be held accountable throughout the process.

The hype around AI is deafening, creating a noisy and confused environment. Organizations are looking for clarity on the role and application of AI in bringing more value to data, and for help avoiding the pitfalls that surround so many of these projects. These pitfalls include error-prone decision making and unintended consequences based on flawed data or “black box” algorithms, and gimmicky approaches including simple search and bolt-on AI that overpromise and under deliver.

If you are a magician specialized in Talend magic, we always hear a key word called Dynamic ingestion of data from various sources to target systems instead of creating individual Talend job for each data flow. In this blog, we will do a quick recap of the concept of Dynamic schema and how we can reorder or shuffle columns when we are employing Dynamic schema in ingestion operations.

Handling secrets has always been a challenging and critical task within organizations.

Data enablement doesn’t stop by delivering data in a data lake or data warehouse. Data must reach easily its point of consumption. Whether it is accessed as a self-service by a business user or integrated into an application, making trusted data available to all when it’s needed is of utmost importance.

To maximize the benefits of cloud, data & machine learning and make it available to innovative digital teams and customers, the data lake is the definite reference model. How can we accelerate its implementation and scope without losing control or jeopardizing its sustainability?

CCPA is the latest regulation which should be a concern for all companies with California residents in their databases. There are less than 90 days left to be ready to comply with CCPA, on top of the other regulations.

Not so long ago, our CFO was looking at changing our financial system and it reminded me of a similar situation that happened when I worked at National Australia Bank many years ago. We changed our entire ERP because the organization needed to slice and dice their data differently. What they could have done instead was use analytics differently to extend the life of the products they had, so that’s what we’ve done.