When most people think of Big Data, often they imagine loads of unstructured data. However, there is always some sort of structure or relationships within this data. Based on these relationships there are one or more representation schemes best suited to handle this type of data. A common pattern seen in the field is hierarchy/relationship representation. This form of representation is adept in handling scenarios like complex business models, chain of event or plans, chain of stock orders in banks,
New R package implyr enables R users to query Impala using dplyr.
Apache Impala (incubating) enables low-latency interactive SQL queries on data stored in HDFS, Amazon S3, Apache Kudu, and Apache HBase. With the availability of the R package implyr on CRAN and GitHub, it’s now possible to query Impala from R using the popular package dplyr.
dplyr provides a grammar of data manipulation,
The Apache HBase Medium Object Storage (MOB) feature was introduced by HBASE-11339. This feature improves low latency read and write access for moderately-sized values (ideally from 100K to 10MB based on our testing results), making it well-suited for storing documents, images, and other moderately-sized objects . The Apache HBase MOB feature achieves this improvement by separating IO paths for file references and MOB objects, applying different compaction policies to MOBs and thus reducing write amplification created by HBase’s compactions.
This article is syndicated with permission from the Apache HBase blog and highlights a collaboration between our partners at Intel and Alibaba engineering in time for “Singles Day“, the biggest shopping day on the net. For more on HBase, mark your calendars! On June 12th, 2017 the Apache HBase community will be hosting their annual HBaseCon.
HBase is the core storage system in Alibaba’s Search Infrastructure.
Zbigniew Baranowski is a database systems specialist and a member of a group which provides and supports central database and Hadoop-based services at CERN. This blog was originally released on CERN’s “Databases at CERN” blog, and is syndicated here with CERN’s permission.
This post presents a performance comparison of few popular data formats and storage engines available in the Apache Hadoop ecosystem: Apache Avro,