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genie - Distributed Big Data Orchestration Service
Genie is a federated job orchestration engine developed by Netflix. Genie provides REST-ful APIs to run a variety of big data jobs like Hadoop, Pig, Hive, Spark, Presto, Sqoop and more. It also provides APIs for managing the metadata of many distributed processing clusters and the commands and applications which run on them.See the official website to find documentation about Genie and specific documentation for various releases.
big-data bigdata orchestration configuration configuration-management spring-boot distributed-systems netflixossspark-py-notebooks - Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.
spark pyspark data-analysis mllib ipython-notebook notebook ipython data-science machine-learning big-data bigdataspark - .NET for Apache® Spark™ makes Apache Spark™ easily accessible to .NET developers.
.NET for Apache Spark provides high performance APIs for using Apache Spark from C# and F#. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write .NET code allowing you to reuse all the knowledge, skills, code, and libraries you already have as a .NET developer.
spark analytics bigdata spark-streaming spark-sql machine-learning fsharp dotnet-core dotnet-standard streaming apache-spark tpcds tpch azure hdinsight databricks emr microsoftvaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀
Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted). HDF5 and Apache Arrow supported.
visualization machine-learning bigdata tabular-data hdf5 machinelearning dataframe memory-mapped-fileApache Doris - A fast MPP database for all modern analytics on big data
Apache Doris is a modern MPP analytical database product. It can provide sub-second queries and efficient real-time data analysis. With it's distributed architecture, up to 10PB level datasets will be well supported and easy to operate. Doris provides batch data loading and real-time mini-batch data loading. It provides high availability, reliability, fault tolerance, and scalability. Its original name was Palo, developed in Baidu.
data-warehouse database bigdata mysql-protocol analytical-database realtime-query scalable paloMobius - C# and F# language binding and extensions to Apache Spark
Mobius provides C# language binding to Apache Spark enabling the implementation of Spark driver program and data processing operations in the languages supported in the .NET framework like C# or F#.For more code samples, refer to Mobius\examples directory or Mobius\csharp\Samples directory.
spark apache-spark rdd dataframe dstream dataset streaming mobius kafka-streaming spark-streaming fsharp bigdata mapreduce eventhubs near-real-timeVoltDB - Fast Scalable SQL DBMS with ACID
VoltDB was specifically designed for contemporary software applications that are pushed beyond their limits by high volume data sources. VoltDB provides the ability to capture, store and process incoming data at millions of read/write operations per second. And VoltDB’s relational model opens that data to be analyzed in real-time, using familiar Business Intelligence tools, to identify data patterns and trends, spot anomalies, or perform tracking and alerting.
database relational acid bigdata scale-out distributed distributed-database oltp analyticsspark-movie-lens - An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset
This Apache Spark tutorial will guide you step-by-step into how to use the MovieLens dataset to build a movie recommender using collaborative filtering with Spark's Alternating Least Saqures implementation. It is organised in two parts. The first one is about getting and parsing movies and ratings data into Spark RDDs. The second is about building and using the recommender and persisting it for later use in our on-line recommender system. This tutorial can be used independently to build a movie recommender model based on the MovieLens dataset. Most of the code in the first part, about how to use ALS with the public MovieLens dataset, comes from my solution to one of the exercises proposed in the CS100.1x Introduction to Big Data with Apache Spark by Anthony D. Joseph on edX, that is also publicly available since 2014 at Spark Summit. Starting from there, I've added with minor modifications to use a larger dataset, then code about how to store and reload the model for later use, and finally a web service using Flask.
movie-recommendation movielens-dataset spark flask big-data bigdatacds - Data syncing in golang for ClickHouse.
Data syncing in golang for ClickHouse. Automatically synchronizing data from MySQL/MongoDB data source to ClickHouse cluster in real time.
clickhouse bigdata kafka-consumerTDengine - Big data platform designed and optimized for the Internet of Things
TDengine is an open-source big data platform designed and optimized for Internet of Things (IoT), Connected Vehicles, and Industrial IoT. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and costs of development and operations.
iot database monitoring time-series bigdata full-stack connected-vehicles industrial-iot time-series-database analytics real-time-analytics column-store columnar-databaseApache Mnemonic - Non-volatile hybrid memory storage oriented library
Apache Mnemonic is a non-volatile hybrid memory storage oriented library, it proposed a non-volatile/durable Java object model and durable computing service that bring several advantages to significantly improve the performance of massive real-time data processing/analytics. developers are able to use this library to design their cache-less and SerDe-less high performance applications.
memory non-volatile-memory cache bigdata memory-storage map-reduceOptimus - :truck: Agile Data Science Workflows made easy with Python and Spark.
Optimus is the missing framework to profile, clean, process and do ML in a distributed fashion using Apache Spark(PySpark). You can go to the 10 minutes to Optimus notebook where you can find the basic to start working.
spark pyspark data-wrangling bigdata big-data-cleaning data-science cleansing data-cleansing data-cleaner apache-spark data-transformationApache Hudi - Streaming Data Lake Platform
Apache Hudi (pronounced Hoodie) stands for Hadoop Upserts Deletes and Incrementals. Hudi manages the storage of large analytical datasets on DFS (Cloud stores, HDFS or any Hadoop FileSystem compatible storage). As an organization, Hudi can help you build an efficient data lake, solving some of the most complex, low-level storage management problems, while putting data into hands of your data analysts, engineers and scientists much quicker.
bigdata stream-processing data-integration datalake spark apachehudi incremental-processing data-lake etlapi.rss - RSS as RESTful. This service allows you to transform RSS feed into an awesome API.
The best RESTful RSS experience you can find. API.RSS turns feed data into an awesome API. The API simplifies how you handle RSS, Atom, or JSON feeds. You can add and keep track of your favourite feed data with a simple, fast and clean REST API. All entries are enriched by Machine Learning and Semantic engines.
rails api sidekiq rss elasticsearch machine-learning rest-api bigdata semantic-web feed rss-feed dandelion-apiSidekick - High Performance HTTP Sidecar Load Balancer
sidekick is a high-performance sidecar load-balancer. By attaching a tiny load balancer as a sidecar to each of the client application processes, you can eliminate the centralized loadbalancer bottleneck and DNS failover management. sidekick automatically avoids sending traffic to the failed servers by checking their health via the readiness API and HTTP error returns.
minio-servers sidecar load-balancer proxy spark bigdata kubernetes miniovaex - Lazy Out-of-Core DataFrames for Python, visualize and explore big tabular data at a billion rows per second
Vaex is a python library for Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (109) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).
dataframe bigdata tabular-data visualization memory-mapped-file hdf5telemetry-batch-view - A Scala framework to build derived datasets, aka batch views, of Telemetry data
This is a Scala application to build derived datasets, also known as batch views, of Telemetry data.Raw JSON pings are stored on S3 within files containing framed Heka records. Reading the raw data in through e.g. Spark can be slow as for a given analysis only a few fields are typically used; not to mention the cost of parsing the JSON blobs. Furthermore, Heka files might contain only a handful of records under certain circumstances.
telemetry spark bigdata mozilla datasetempujar - When you need to push data around, you push it. A node.js ETL tool.
When you need to push data around, you push it. Push it real good. An ETL and Operations tool.Empujar's top level object is a "book", which contains "chapters" and then "pages". Chapters are excecuted 1-by-1 in order, and then each page in a chapter can be run in parallel (up to a threading limit you specify).
data big bigdata redshift amazon mysql aurora ftp elasticsearch etl elt job transform parallelbig-data-rosetta-code - Code snippets for solving common big data problems in various platforms
Code snippets for solving common big data problems on various platforms. Inspired by Rosetta Code.Copyright 2016 Spotify AB.
scio spark scalding bigdata
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