Notes about Cutting-Edge Technologies and Everything

  Data helps companies take the guesswork out of decision-making. Teams can use data-driven evidence to decide which products to build, which features to add, and which growth initiatives to pursue. And, such insights-driven businesses grow at an annual rate of over 30%. But, there’s a difference between being merely data-aware and insights-driven. Discovering insights requires finding a way to analyze data in near real-time, which is where cloud data warehouses play a vital role. As scalable repositories of data, warehouses allow businesses to find insights by storing and analyzing huge amounts of structured and semi-structured data. And, running a data warehouse is more than a technical initiative. It’s vital to the overall business strategy and can inform an array of future product, marketing, and engineering decisions. But, choosing a cloud data warehouse provider can be challenging. Users have to evaluate costs, performance, the ability to handle real-time workloads, and other...

This is a major release that includes many new features, performance improvements, and hundreds of stability enhancements - see the highlights and more details below. • Elasticsearch 6.  Dremio now supports the latest versions of Elasticsearch. Enjoy full SQL support, including JOINs, Window functions, and accelerated analytics through any BI tool, including Tableau and Power BI. We also added support for compressing Elasticsearch responses to minimize network traffic.  • Approximate count distinct acceleration.  Dremio now supports accelerating count distinct queries based on an approximation-based algorithm (HyperLogLog). This provides a faster and more memory efficient way of providing distinct counts and is especially useful in high cardinality scenarios with very large datasets.  • Faster ORC performance.  Data encoded in ORC is now significantly faster to access and more memory efficient for ORC managed in Hive sources.  • Support for AWS GovClou...

Actian Vector in Hadoop (VectorH for short) is a new SQL-on-Hadoop  system  built  on  top  of  the  fast  Vectorwise  analytical database system.  VectorH achieves fault tolerance and storage scalability by relying on HDFS, and extends the state-of-the-art in SQL-on-Hadoop systems by instrumenting  the  HDFS  replication  policy  to  optimize  read  locality. VectorH integrates with YARN for workload management, achieving a high degree of elasticity.  Even though HDFS is an append-only filesystem, and VectorH supports (update-averse) ordered tables, trickle updates are possible thanks to Positional Delta Trees (PDTs), a differential update structure that can be queried efficiently.  The paper describes the changes made to single-server Vectorwise to turn it into a Hadoop-based  MPP  system,  encompassing  workload  management, parallel  query  optimizat...