Systems | Development | Analytics | API | Testing

Corporations are generating unprecedented volumes of data, especially in industries such as telecom and financial services industries (FSI). Many organizations are hoping to leverage these massive amounts of data by investing heavily in big data solutions – solutions that they hope can meet business goals such as increasing customer satisfaction, uncovering alternative revenue streams, or improving operational efficiency.

Pub/Sub’s ingestion of data into BigQuery can be critical to making your latest business data immediately available for analysis. Until today, you had to create intermediate Dataflow jobs before your data could be ingested into BigQuery with the proper schema. While Dataflow pipelines (including ones built with Dataflow Templates) get the job done well, sometimes they can be more than what is needed for use cases that simply require raw data with no transformation to be exported to BigQuery.

As Business Intelligence (BI) tools, data warehousing solutions, and enterprise data and application landscapes have advanced, it’s worth taking the time to rethink that old model, starting with the dichotomy between operational reporting (OR) and strategic analytics. There is a clear difference between operational reporting and BI, but they can and should work together. Companies use one or the other, and if they have both, there is a gap between them.

Snowflake’s Data Cloud is powered by a single engine. From day 1, we have been focusing on consistently evolving and improving this engine to allow existing workloads to run more efficiently and enable new workloads to run on Snowflake. The single engine approach translates into a single experience—from one consistent pricing model to an integrated approach combining performance, security, governance, and the foundation to seamlessly enable cross-region or cross-cloud scenarios.

Angles for Oracle simplifies the process of accessing data from Oracle ERPs for reporting and analytical insights; offering seamless integration with cloud data warehouse targets.

As data science has taken center stage in a lot of organizations, many are relearning what they’ve already known – that dry, mathematical calculations don’t inspire and don’t stick. It’s the story that matters. In this first of a two-part blog series, we look at the history and neuroscience of storytelling and how it can help us understand data at a more human level.