Data professionals face an efficiency gap; they spend too much time to get access to the data they need and then put it into the appropriate business context. The capacity of delivering trusted data to business experts at the point of need is critical if you want to liberate data value within your company.

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.

We’ve been thrilled to recently bring Tereza Nemassanyi to our team as our new CRO. Tereza brings more than just experience to the Keboola team from Microsoft - her unique perspective and constant devotion to speaking on innovation is the kind of thought leadership that excites us. We sat down with Tereza to get the scoop on her approach to innovation and how she thinks the innovation backlog can be the key to business growth.

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.