Today we’re announcing a public preview for the BigQuery native JSON data type, a capability which brings support for storing and analyzing semi-structured data in BigQuery. With this new JSON storage type and advanced JSON features like JSON dot notation support, adaptable data type changes, and new JSON functions, semi-structured data in BigQuery is now intuitive to use and query in its native format.

With the amount of data being created yearly reaching in the dozens of zettabytes and growing rapidly from year to year, companies have had to find creative solutions to manage big data. That is why loading terabytes of data in minutes or seconds for processing may be absolutely necessary for some businesses. This is the kind of speed and functionality that BigQuery gives. So, what is BigQuery? The Google Cloud suite offers a wide variety of services for every industry.

The start of a new year is a great time for reflection, and, when I look at the progress organizations are making on their data journey, I’m feeling quite positive as we welcome 2022.

While COVID-19 continues to cause devastating disruption to the global economy more than two years into the pandemic, it is also continuing to force remarkable innovation across different industries. Companies have found new ways to sell, service and operate during the crisis. For me, there is one common theme for these innovative companies, including Qlik, and it is “Jobs to be Done.”

Since I’m now migrating NodeGraph’s processes to Qlik, I thought it may be a good time to talk about migrating data during a merger or acquisition. There are many aspects to consider. Here are some of my thoughts on why companies merge or migrate data landscapes, common M&A migration pitfalls and how to avoid them, the time and cost involved migrating data during a merger or acquisition, and other topics.

Data is everywhere. As the sheer volume and number of data sources continue to explode, so do new opportunities for modern businesses to create and act on insights. That is if they are equipped with the right analytics technology. Historically, many businesses have settled for “good enough” analytics tools, putting up with lackluster bundles from full-stack vendors in an attempt to minimize cost or risk.

Out with the old; in with the new! If you haven’t already checked out the new Snowflake® interface (aka Snowsight®), make it your New Year’s resolution. Set yourself up for success in 2022 by spending a few minutes getting to know the new features and experiences that are in public preview—available when you click the Snowsight button at the top of your console’s menu bar.

With interest in big data and cloud increasing around the same time, it wasn’t long until big data began being deployed in the cloud. Big data comes with some challenges when deployed in traditional, on-premises settings. There’s significant operational complexity, and, worst of all, scaling deployments to meet the continued exponential growth of data is difficult, time-consuming, and costly.

Since the release of Cloudera Data Engineering (CDE) more than a year ago, our number one goal was operationalizing Spark pipelines at scale with first class tooling designed to streamline automation and observability. In working with thousands of customers deploying Spark applications, we saw significant challenges with managing Spark as well as automating, delivering, and optimizing secure data pipelines.