Quill for Scala 3
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Updated
May 24, 2024 - Scala
Quill for Scala 3
Data Accelerator for Apache Spark simplifies onboarding to Streaming of Big Data. It offers a rich, easy to use experience to help with creation, editing and management of Spark jobs on Azure HDInsights or Databricks while enabling the full power of the Spark engine.
A JupyterLab extension providing, SQL formatter, auto-completion, syntax highlighting, Spark SQL and Trino
This repository is used to perform data analysis using Databricks and Tableau on NYC crime datasets
Contains an analysis of key home sales metrics using SparkSQL and Python to manage large amounts of data.
基于Spring Boot全家桶打造,大数据PAAS组件适配器,一键适配DolphinScheduler、Hadoop、Spark、Hive、Impala、HBase、Kafka、Doris、StarRocks、ClickHouse、Neo4j、Redis、ElasticSearch,通过标准REST接口和SQL语句操作,简单易用,方便二次开发和快速集成
Process Common Crawl data with Python and Spark
In pursuit of significant metrics for home sales data, Google Colab and SparkSQL were employed to extract essential insights.
Use SparkSQL to determine key metrics of the data. Use Spark to create temporary views, partition the data, cache and uncache a temporary table, and verify that the table has been uncached.
Geospatial Raster support for Spark DataFrames
Weather Data Analysis using Python, Pandas, SparkSQL, AutoRegression Model
This project contains the learning and experiments with the Apache Spark.
Run your first analysis project on Apache Zeppelin using Scala (Spark), Shell, and SQL
Use PySpark and SparkSQL to execute SQL queries through a temporary view of the DataFrame created. Conduct additional queries on cached and partitioned data to determine runtime comparisons.
Coursera IBM Data Engineering (Course 12 from 13)
A rudimentary command line utility for contrasting Apache Spark event logs.
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