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Hadoop technology is everywhere. For better or worse, Hadoop has become synonymous with big data. In just a few years, Hadoop has become a de facto standard from an edge technology. It seems that not only is Hadoop now a standard for corporate data, but its position in the future seems to be hard to shake. Google file system and MapReduce let's explore the soul of Hadoop--mapreduce. Faced with the explosive growth of data, ...

Analysis is the core of all enterprise data deployments. Relational databases are still the best technology for running transactional applications (which is certainly critical for most businesses), but when it comes to data analysis, relational databases can be stressful. The adoption of an enterprise's Apache Hadoop (or a large data system like Hadoop) reflects their focus on performing analysis, rather than simply focusing on storage transactions. To successfully implement a Hadoop or class Hadoop system with analysis capabilities, the enterprise must address some of the following 4 categories to ask ...

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In the current enterprise IT architecture, system administrators and DBAs will consider using the NoSQL database to solve problems that the RDBMS cannot solve, especially the internet industry. Traditional relational databases store data mainly as tables (table), and cannot meet the challenge of unstructured data. In the process of data standardization, the performance of relational database is encountering bottleneck. NoSQL, as its name suggests, is Not-only SQL, which can be a good complement to relational databases. In the report before the TechTarget database, ...

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The term “big data” has been on fire for several years. In the last one or two years, the limelight seems to have been taken away by the concepts of artificial intelligence and deep learning, and has gradually become a “out of gas” technical vocabulary.

The operating language of the data is SQL, so many tools are developed with the goal of being able to use SQL on Hadoop. Some of these tools are simply packaged on top of the MapReduce, while others implement a complete data warehouse on top of the HDFs, while others are somewhere between the two. There are a lot of such tools, Matthew Rathbone, a software development engineer from Shoutlet, recently published an article outlining some common tools and scenarios for each tool and not ...

MAPR today updated its Hadoop release, adding Apache Drill 0.5 to reduce the heavy data engineering effort. Drill is an open source distributed ANSI query engine, used primarily for self-service data analysis. This is the open source version of Google's Dremel system, which is used primarily for interactive querying of large datasets-which support its bigquery servers. The objective of the Apache Drill project is to enable it to scale to 10,000 servers or more servers, while processing in a few seconds ...