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Apache Pinot 1.0 Provides a Realtime Distributed OLAP Datastore
Apache Pinot is an open source column-oriented distributed data store written in Java. Pinot is designed to use Online Analytical processing (OLAP) in order to answer multi-dimensional analytical (MDA) queries with low latency.
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Instacart Creates a Self-Serve Apache Flink Platform on Kubernetes
Instacart moved their Apache Flink workloads from AWS EMR to Kubernetes to meet the high demand for data processing use cases using Flink within the organization, as using EMR became problematic for many teams with different requirements. As a result, they made the platform easier to use and reduced their operational and infrastructure costs.
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Grab Shared Its Experience in Designing Distributed Data Platform
GrabApp is an application that customers select and buy their daily needs from merchants. To be scalable and manageable the data platform and ingestion should be designed as a distributed, fault-tolerant. To design this data platform two classes of data stores are considered: OLTP and OLAP.
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Fitting Presto to Large-Scale Apache Kafka at Uber
The need for ad-hoc real-time data analysis has been growing at Uber. They run a large Apache Kafka deployment and need to analyse data going through the many workflows it supports. Solutions like stream processing and OLAP datastores were deemed unsuitable. An article was published recently detailing why Uber chose Presto for this purpose and what it had to do to make it performant at scale.
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From Natural Language Queries to Insights: GCP BigQuery Data QnA Usage in Twitter
The Twitter engineering team has shared architectural details of their Qurious data insights platform and its advantages for real-time analysis. Designed for internal business customers, the platform allows users to analyze Twitter’s BigQuery data using natural language queries and create dashboards.
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Amazon QuickSight Q: Answers to Business Intelligence Questions
Amazon recently announced the general availability of QuickSight Q, a natural language query tool for the enterprise edition of the managed business intelligence service QuickSight.
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Improving Azure SQL Database Performance Using In-Memory Technologies
In late 2016, Microsoft announced the general availability of Azure SQL Database In-Memory technologies. In-Memory processing is only available in Azure Premium database tiers and provides performance improvements for On-line Analytical Processing (OLTP), Clustered Columnstore Indexes and Non-clustered Columnstore Indexes for Hybrid Transactional and Analytical Processing (HTAP) scenarios.
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Microsoft Releases Power BI Embedded Preview
Recently at the 2016 Build Event in San Francisco, Microsoft announced a change to their Power BI offering. The update comes in the form of giving customers and ISVs with the ability to embed Power BI reports within their own applications. Microsoft is calling this service Power BI Embedded and it is currently in preview.
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Olap4j 1.0: a Java API for OLAP Servers
Business Intelligence vendor Pentaho has announced the release of olap4j 1.0, a new, common Java API for any online analytical processing (OLAP) server.
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Column-based Storage in SQL Server 2011
Imagine ad hock data mining queries against a single table with 1 TB of data and 1.44 billion rows coming back in roughly a second. This is the scenario Microsoft intends to support using 32-core machines and their new column-based storage engine.