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?