Three tech leaders discuss the future of analytics and data architecture — and how to get the most value from them.

Customers today expect feature-reach and user-friendly access to technology that makes their lives easier. They expect to use this technology to engage with companies anywhere, anytime, and with any device. Organizations must be able to not just meet, but exceed, these expectations. Failing to do so could result in lost revenue as customers move on to competitors.

If your organization is using multi-tenant big data clusters (and everyone should be), do you know the usage and cost efficiency of resources in the cluster by tenants? A chargeback or showback model allows IT to determine costs and resource usage by the actual analytic users in the multi-tenant cluster, instead of attributing those to the platform (“overhead’) or IT department. This allows you to know the individual costs per tenant and set limits in order to control overall costs.

Cloudera Data Platform (CDP) supports access controls on tables and columns, as well as on files and directories via Apache Ranger since its first release. It is common to have different workloads using the same data – some require authorizations at the table level (Apache Hive queries) and others at the underlying files (Apache Spark jobs). Unfortunately, in such instances you would have to create and maintain separate Ranger policies for both Hive and HDFS, that correspond to each other.

Learn from 20 marketing, sales and ad experts how attribution reports can help you understand your customer’s interactions with your company.

This blog is part of our ongoing "meet the analyst of the future" series, which profiles analysts who are transforming their organizations and supercharging their careers by embracing the future of analytics today.

Today, I’m thrilled to announce that Lenses.io is joining Celonis, the leader in execution management. Together we will raise the bar in how businesses are run by driving them with real-time data, making the power of streaming open, operable and actionable for organizations across the world. When Lenses.io began, we could never have imagined we’d reach this moment.

Apache Kafka has grown from an obscure open-source project to a mass-adopted streaming technology, supporting all kinds of organizations and use cases. Many began their Apache Kafka journey to feed a data warehouse for analytics. Then moved to building event-driven applications, breaking down entire monoliths. Now, we move to the next chapter. Joining Celonis means we’re pleased to open up the possibility of real-time process mining and business execution with Kafka.

For decades, ETL (extract, transform, load) has been a mainstay of organizations’ data integration workflows, moving information from various sources to a centralized data warehouse. More recently, however, businesses have been shaking things up with reverse ETL, which flips the sources and targets by sending data from your warehouse to third-party systems like Zendesk.