Cloudera has been providing enterprise support for Apache NiFi since 2015, helping hundreds of organizations take control of their data movement pipelines on premises and in the public cloud. Working with these organizations has taught us a lot about the needs of developers and administrators when it comes to developing new dataflows and supporting them in mission-critical production environments.

Joint customers can move faster with more visibility across their entire data stack, helping data teams collaborate better with increased trust in their data.

The growth of big data has scattered data sources throughout the enterprise, tucking them away in silos and making them hard to find. But there is more to data than just storing information in an operational data store or running analytics from a data warehouse. For example, regulated industries have requirements needing tighter control over the data.

The world is full of different types of goals. Consider football. The goal in football is at the end of the field. A runner either crosses the goal or they don't, when trying to make a touchdown. Or, consider basketball. In basketball, when a player shoots the ball, the player’s shot either goes through the net or it doesn’t. Alternatively, consider ice hockey. When a hockey player shoots the puck, it either goes into the net — or it doesn’t.

In the past 10 years, the term ‘disruption’ has been abuzz across business and industry circles. Whether succeeding as a disruptive innovator or defending against a challenger as an incumbent, the nature of innovation has focused on the ability to re-imagine and execute business models. The nature of such business models has shifted the trajectory of demand either to totally non-existent markets or delivered from low-end footholds to scale.

You'll need to understand the six key components of a data pipeline and overcome five important technical challenges.

Any data-driven organization will tell you that the holy grail is faster time to insights. But the unfortunate truth is that business users often have to wait days — even weeks or months — to analyze the data they need. Behind the scenes, data engineering teams put a lot of work into joining disparate datasets, creating pipelines, and delivering a final data product back to their stakeholders for analysis.

Whether on-premises, private, hybrid or multicloud, or at the edge – working in the cloud is complex. And as the enterprise expands, so too does the threat surface for cyberattacks. Ransomware, in particular, is among the biggest risks organizations now face and cloud-based data is accounting for 39% of successful attacks.