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Creating a Secure Distributed Database Cluster Leveraging Your Existing Database Management System
The emergence of Big Data and data lakes, doesn't necessarily mean the disappearance of the trusted relational database. The two can coexist, relational databases just need to adjust. For the transition we propose Database Plus, a new technology & concept applicable to any database, that answers these challenges and eliminates switching costs and vendor lock-in.
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Introduction to Apache Beam Using Java
Apache Beam is a stream processor, helping developers migrate work between different processes to offload work onto runners that leverage external resources.
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The Next Evolution of the Database Sharding Architecture
In this article, author Juan Pan discusses the data sharding architecture patterns in a distributed database system. She explains how Apache ShardingSphere project solves the data sharding challenges. Also discussed are two practical examples of how to create a distributed database and an encrypted table with DistSQL.
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Building Latency Sensitive User Facing Analytics via Apache Pinot
At QCon, a virtual conference for senior software engineers and architects covering the trends, Chinmay Soman talked about how you can use Apache Pinot as part of your data pipelines for building rich, external, or site-facing analytics.
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Indestructible Storage in the Cloud with Apache Bookkeeper
At Salesforce, we required a storage system that could work with two kinds of streams, one stream for write-ahead logs and one for data. But we have competing requirements from both of the streams. Being the pioneers in cloud computing, we also required our storage system to be cloud-aware as the requirements of availability and durability are ever more increasing.
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Apache Beam Interview with Frances Perry
InfoQ Interviews Apache Beam's Frances Perry about the impetus for using Beam and the future of the top-level open source project and covers the thoughts behind the programming model as well as some of the touch-points in integration with other data engineering tools like Apache Spark and Flink.
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From Alibaba to Apache: RocketMQ’s Past, Present, and Future
Feng Jia and Wang Xiaorui share the core distributed systems principals behind RocketMQ, Alibaba's distributed messaging and data streaming platform now open sourced through the Apache Foundation.
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Article Series: Getting a Handle on Data Science as a Software Developer
Software developers and managers are realizing that they need data science among their skills, to be able to tackle pressing problems. In this series, field experts provide guidance to help us navigate among the available data analysis options. They explore ways of understanding where data science is needed and where it’s not, and how to turn it into an asset.
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Case Study: Selecting Big Data and Data Science Technologies at a large Financial Organisation
Adopting Big Data and Data Science technologies into an organisation is a transformative project similar to an agile transformation and with many similar challenges. In this article, the author describes such a project for a FTSE100 financial services company.
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Q&A: Relevant Search with Elasticsearch and Solr
In their book "Relevant Search", Doug Turnbull and John Berryman focus on the challenge of providing search results by balancing the needs and intents of the user. Using Elasticsearch and Solr, relevance engineers can constantly tune the needs of the business vs. the needs of the user.
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Bridging Microsoft Word and the Browser
HTML editors work fine for general formatting, but they don’t have all the capabilities that some businesses require. Creating graphics, diagrams, tracking changes and inserting comments are useful and come out of the box in Microsoft Word In this article, Prasadu Babu Dandu shows how to serve up Word documents as HTML.
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What is Apache Tez?
Apache Tez is a new distributed execution framework that is targeted to-wards data-processing applications on Hadoop. But what exactly is it? How does it work? In the presentation, “Apache Tez: Accelerating Hadoop Query Processing”, Bikas Saha and Arun Murthy discuss Tez’s design, highlight some of its features and share initial results obtained by making Hive use Tez instead of MapReduce.