Spark

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Data partitioning is critical to data processing performance especially for large volume of data processing in Spark. Partitions in Spark won’t span across nodes though one node can contains more than one partitions. When processing, Spark assigns one task for each partition and each worker threads ...

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Spark is an analytics engine for big data processing. There are various ways to connect to a database in Spark. This page summarizes some of common approaches to connect to SQL Server using Python as programming language. For each method, both Windows Authentication and SQL Server ...

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From Spark 2.0, you can easily read data from Hive data warehouse and also write/append new data to Hive tables. This page shows how to operate with Hive in Spark including: Create DataFrame from existing Hive table Save DataFrame to a new Hive table Append data to the existing Hive table via ...

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Apache Arrow is an in-memory columnar data format that can be used in Spark to efficiently transfer data between JVM and Python processes. This currently is most beneficial to Python users that work with Pandas/NumPy data. In this article, I'm going to show you how to utilise Pandas UDF in ...

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In my previous post about Data Partitioning in Spark (PySpark) In-depth Walkthrough , I mentioned how to repartition data frames in Spark using repartition or coalesce functions. In this post, I am going to explain how Spark partition data using partitioning functions. Partitioner class is ...

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The page summarizes the steps required to run and debug PySpark (Spark for Python) in Visual Studio Code. Install Python from the official website: https://www.python.org/downloads/ . The version I am using is 3.6.4 32-bit. Pip is shipped together in this version. Download Spark 2.3.3 from ...

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This article shows how to convert a Python dictionary list to a DataFrame in Spark using Python. data = [{"Category": 'Category A', "ID": 1, "Value": 12.40}, {"Category": 'Category B', "ID": 2, "Value": 30.10}, {"Category": 'Category C', "ID": 3, "Value": 100.01} ] The ...

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Schema evolution is supported by many frameworks or data serialization systems such as Avro, Orc, Protocol Buffer and Parquet. With schema evolution, one set of data can be stored in multiple files with different but compatible schema. In Spark, Parquet data source can detect and merge schema of ...

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In my previous article about  Connect to SQL Server in Spark (PySpark) , I mentioned the ways to read data from SQL Server databases as dataframe using JDBC. We can also use JDBC to write data from Spark dataframe to database tables. In the following sections, I'm going to show you how to ...

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Spark 3.0.0 was release on 18th June 2020 with many new features. The highlights of features include adaptive query execution, dynamic partition pruning, ANSI SQL compliance, significant improvements in pandas APIs, new UI for structured streaming, up to 40x speedups for calling R user-defined ...