As organizations look to get smarter and more agile in how they gain value and insight from their data, they are now able to take advantage of a fundamental shift in architecture. In the last decade, as an industry, we have gone from monolithic machines with direct-attached storage to VMs to cloud. The main attraction of cloud is due to its separation of compute and storage – a major architectural shift in the infrastructure layer that changes the way data can be stored and processed.

In the lifecycle of a data warehouse in production, there are a variety of tasks that need to be executed on a recurring basis. To name a few concrete examples, scheduled tasks can be related to data ingestion (inserting data from a stream into a transactional table every 10 minutes), query performance (refreshing a materialized view used for BI reporting every hour), or warehouse maintenance (executing replication from one cluster to another on a daily basis).

The value of YouTube has grown significantly for companies looking to bolster their brands with video content. The YouTube API is report-based, and its prebuilt reports fall into one of two categories: channel reporting and content owner reporting. Channel reports refer to the videos on a specific YouTube channel, while content owner reports contain data on all the channels owned by a particular individual.

Copyright quickly migrates its on-prem databases into Snowflake cloud using Fivetran. By implementing the modern data stack, and decommissioning numerous legacy platforms, Copyright estimates a savings of $40,000 a year and finds it much easier to manage the workload.

Our 1.2.0.0 release of Cloudera Streaming Analytics Powered by Apache Flink brings a wide range of new functionality, including support for lineage and metadata tracking via Apache Atlas, support for connecting to Apache Kudu and the first iteration of the much-awaited FlinkSQL API. Flink’s SQL interface democratizes stream processing, as it caters to a much larger community than the currently widely used Java and Scala APIs focusing on the Data Engineering crowd.

In these uncertain times of the COVID-19 crisis, one thing is certain – data is key to decision making, now more than ever. And, the need for speed in getting access to data as it changes has only accelerated. It’s no wonder, then, that organisations are looking to technologies that help solve the problem of streaming data continuously, so they can run their businesses in real-time.

Worldwide, businesses are adapting to the new market conditions by transforming their current operating models to meet the new consumer demands and improve productivity, all while still focusing on achieving growth. In this new era, taking an outside-in approach to digital business ecosystems can help organizations harness their existing resources and relationships to drive new innovations and efficiency.

A product, an application, a website, the success of all these do depend on the functionalities built into them. But answer to some questions like “How easy they were to use? How easy were they to understand? Did they do the job without any errors?”, ‘quality’ becomes the most important factor of it all. A developer may build the functionality but a tester determines the quality of the software and how well they were built.