Apache ORC
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Comparaison of different file formats in Big Data
Categories: Big Data, Data Engineering | Tags: Analytics, Avro, HDFS, Hive, MapReduce, ORC, Batch processing, Big Data, CSV, Data Analytics, Data structures, Database, JSON, Protocol Buffers, Hadoop, Kafka, Parquet, Spark, Kubernetes, XML
In data processing, there are different types of files formats to store your data sets. Each format has its own pros and cons depending upon the use cases and exists to serve one or several purposes…
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Data Lake ingestion best practices
Categories: Big Data, Data Engineering | Tags: Avro, Hive, NiFi, ORC, Data Governance, HDF, Operation, Protocol Buffers, Spark, Data Lake, File Format, Registry, Schema
Creating a Data Lake requires rigor and experience. Here are some good practices around data ingestion both for batch and stream architectures that we recommend and implement with our customers…
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What's new in Apache Spark 2.3?
Categories: Data Engineering, DataWorks Summit 2018 | Tags: Arrow, ORC, PySpark, Streaming, Tuning, Spark, Spark MLlib, Data Science, Docker, Kubernetes, pandas
Let’s dive into the new features offered by the 2.3 distribution of Apache Spark. This article is a composition of the following talks seen at the DataWorks Summit 2018 and additional research: Apache…
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Essential questions about Time Series
Categories: Big Data | Tags: Druid, Hive, ORC, Elasticsearch, Graphana, IOT, HBase, Data Science
Today, the bulk of Big Data is temporal. We see it in the media and among our customers: smart meters, banking transactions, smart factories, connected vehicles … IoT and Big Data go hand in hand. We…
By David WORMS
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HDFS and Hive storage - comparing file formats and compression methods
Categories: Big Data | Tags: Analytics, Hive, ORC, Parquet, File Format
A few days ago, we have conducted a test in order to compare various Hive file formats and compression methods. Among those file formats, some are native to HDFS and apply to all Hadoop users. The…
By David WORMS
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