Category Archives: Hadoop

datadotzweekly

DataDotz Bigdata Weekly

APACHE SPARK
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Using Apache Spark for large-scale language model training

Facebook has written about their experience converting their n-gram language model training pipeline from Apache Hive to Apache Spark. The post describes their Hive-based solution, their Spark-based solution, and the scalability challenges Continue reading

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datadotzweekly

DataDotz Bigdata Weekly

Replicating Relational Databases with Stream Sets Data Collector
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Relational Databases with Stream DataSets

StreamSets Data Collector has long supported both reading and writing data from and to relational databases via Java Database Connectivity (JDBC). While it was straightforward to configure pipelines to read data from individual tables, ingesting records from an entire database was cumbersome, requiring a pipeline per table. Continue reading

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datadotzweekly

DataDotz Bigdata Weekly

Apache Apex
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SQL on Apache Apex

Big Data has an interesting history. In the past few years, massive amounts of data have been generated for processing and analytics, and enterprises have been facing problems processing ever increasing data size. In order to process this increasing data size, the way was to scale up but scaling up was costly and resulted in vendor lock-in. So they started to look at scaling out. Enter the Big Data ecosystem with projects like Hadoop, YARN, Spark which fairly satisfied Big Data processing needs. Continue reading

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datadotzweekly

DataDotz Bigdata Weekly

Apache Spark
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ETL with Apache Spark
A common design pattern often emerges when teams begin to stitch together existing systems and an EDH cluster: file dumps, typically in a format like CSV, are regularly uploaded to EDH, where they are then unpacked, transformed into optimal query format, and tucked away in HDFS where various EDH components can use them. When these file dumps are large or happen very often, these simple steps can significantly slow down an ingest pipeline. Continue reading

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datadotzweekly

DataDotz Bigdata Weekly

Apache Oozie
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Use the New Apache Oozie Database Migration Tool
The Apache Oozie server is a stateless web application by design, with all information about running and completed workflows, coordinator jobs, and bundle jobs stored in a relational database. Prior to Cloudera Manager 5.4, Oozie was configured to use the embedded Apache Derby database for this purpose by default. Continue reading

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hadoop-stodgy

HDFS NameNode High Availability in Hadoop 2.x – Part 1

Hadoop is the foundation of most big data architectures. Hadoop 1 popularized MapReduce programming for batch jobs and demonstrated the potential value of large scale, distributed processing. Though MapReduce was intensive and played a vital role, it was not suitable for interactive analysis, and constrained in support for graph, machine learning and on other memory intensive algorithms.

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hbase_logo

Moving from HBase 0.94 to Hbase 0.98

Version difference between Hbase 0.94 – Hbase 0.96:

Hbase 0.96 is more than a year of making. Some of the major improvements in this version are

  • Improved Stability: The node count configurability, data sizing, duration and more turned up on more bugs when we try to do scan or fetch. This has been fixed by introducing the table locks for cross cluster alterations and cross-row transaction support

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