Schlagwort: spark
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From Structured Query Languages (SQL) to Dataframe Languages (DL)
Structured Query Languages (SQL) exists since the 1970s and have been first standardized around 1986 by the American National Standards Institute (ANSI). Their purpose was to have a human-understandable language to query data in tables in database management systems. This means SQL is a domain-specific language. Much later they have been also adopted to query…
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Revisiting Big Data Formats: Apache Iceberg, Delta Lake and Apache Hudi
Novel Big Data formats, such as Apache Parquet, Apache ORC or Apache Avro have been years ago the game changer for processing massive amounts of data efficiently as I wrote in a previous blog post (aside of the Big Data platforms leveraging them). Nowadays we see the emergence of new Big Data formats, such as…
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Automated Machine Learning (AutoML) and Big Data Platforms
Although machine learning exists already since decades, the typical data scientist – as you would call it today – would still have to go through a manual labor-intensive process of extracting the data, cleaning, feature extraction, regularization, training, finding the right model, testing, selecting and deploying it. Furthermore, for most machine learning scenarios you do…
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Big Data Analytics on Excel files using Hadoop/Hive/Flink/Spark
Today we have released HadoopOffice v1.1.0 with major enhancements: Based on the latest Apache POI 3.17 Apache Hive: Query Excel files and write tables to Excel files using the Hive Serde Apache Flink support for Flink Table API and Flink DataSource/DataSink Signing and verification of signatures of Excel files Example to use the HadoopOffice library…
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HadoopOffice – A Vision for the coming Years
HadoopOffice is already since more than a year available (first commit: 16.10.2016). Currently it supports Excel formats based on the Apache POI parsers/writers. Meanwhile a lot of functionality has been added, such as: Support for .xlsx and .xls formats – reading and writing Encryption/Decryption Support Support for Hadoop mapred.* and mapreduce.* APIs Support for Spark…
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Ethereum & Analytics: Explore the blockchain using Hadoop, Hive, Flink and Spark
HadoopCryptoLedger release 1.1.0 added support for another well-known cryptocurrency: Ethereum and its Altcoins. Of course similar to its Bitcoin & Altcoin support you can use the library with many different frameworks in the Hadoop ecosystem: Hadoop MR Apache Hive Apache Flink Apache Spark and Apache Spark Datasource API Furthermore, you can use it with various…
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Big Data Analytics on Bitcoin‘s first Altcoin: NameCoin
This blog post is about analyzing the Namecoin Blockchain using different Big Data technologies based on the HadoopCryptoLedger library. Currently, this library enables you to analyze the Bitcoin blockchain and Altcoins based on Bitcoin (incl. segregated witness), such as Namecoin, Litecoin, Zcash etc., on Big Data platforms, such as Hadoop, Hive, Flink and Spark. A…
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Templates, low footprint mode, improved integration with Spark for the HadoopOffice library for reading/writing Excel files on Big data platforms
Although it seems to be that it was only a small improvement, version 1.0.4 of the HadoopOffice library has a lot of new features for reading/writing Excel files: Templates, so you can define complex documents with diagrams or other features in MSExcel and fill it with data or formulas from your Big Data platform in…
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Reading/Writing Excel documents with the HadoopOffice library on Hadoop and Spark – First release
Reading/Writing office documents, such as Excel, has been always challenging on Big data platforms. Although many libraries exist for reading/writing office documents, they have never been really integrated in Hadoop or Spark and thus lead to a lot of development efforts. There are several use cases for using office documents jointly with Big data technologies:…