Search Results for "hadoop"
Sort By:
Showing 81 open source projects for "hadoop"
View related business solutions-
SKUDONET ADC, operates at the application layer, efficiently distributing network load and application load across multiple servers. This not only enhances the performance of your application but also ensures that your web servers can handle more traffic seamlessly.
-
Engage is a pre-built, intelligent contact center platform that transforms customer service.
-
1
Apache HBase
Get random, realtime read/write access to your Big Data
... HBase provides Bigtable-like capabilities on top of Hadoop and HDFS. Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options. Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX. Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables. -
2
ANTLR
Parser generator to read, process, or translate structured text
... and Pig, the data warehouse and analysis systems for Hadoop, both use ANTLR. Lex Machina uses ANTLR for information extraction from legal texts. Oracle uses ANTLR within SQL Developer IDE and their migration tools. NetBeans IDE parses C++ with ANTLR. The HQL language in the Hibernate object-relational mapping framework is built with ANTLR. -
3
syslog-ng
Log management solution that improves the performance of SIEM
... to Hadoop, Elasticsearch, MongoDB, and Kafka as well as many others. syslog-ng flexibly routes log data from X sources to Y destinations. Instead of deploying multiple agents on hosts, organizations can unify their log data collection and management. syslog-ng Store Box provides automated archiving, tamper-proof encrypted storage, granular access controls to protect log data. The largest appliance can store up to 10TB of raw logs. -
4
Apache Impala
Apache Impala
Impala provides low latency and high concurrency for BI/analytic queries on the Hadoop ecosystem, including Iceberg, open data formats, and most cloud storage options. Impala also scales linearly, even in multitenant environments. Impala is integrated with native Hadoop security and Kerberos for authentication, and via the Ranger module, you can ensure that the right users and applications are authorized for the right data. Utilize the same file and data formats and metadata, security... -
Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers in a couple of ways, either through text or with synthetic speech. Dialogflow CX and ES provide virtual agent services for chatbots and contact centers. If you have a contact center that employs human agents, you can use Agent Assist to help your human agents. Agent Assist provides real-time suggestions for human agents while they are in conversations with end-user customers.
-
5
Apache Phoenix
Mirror of Apache Phoenix
Apache Phoenix is a SQL skin over HBase delivered as a client-embedded JDBC driver targeting low latency queries over HBase data. Apache Phoenix enables OLTP and operational analytics in Hadoop for low latency applications by combining the best of both worlds. The power of standard SQL and JDBC APIs with full ACID transaction capabilities and the flexibility of late-bound, schema-on-read capabilities from the NoSQL world by leveraging HBase as its backing store. Apache Phoenix is fully... -
6
SageMaker Spark
A Spark library for Amazon SageMaker
... trained models, and, if you have your own ML algorithms built into SageMaker compatible Docker containers, you can use SageMaker Spark to train and infer on DataFrames with your own algorithms -- all at Spark scale. SageMaker Spark depends on hadoop-aws-2.8.1. To run Spark applications that depend on SageMaker Spark, you need to build Spark with Hadoop 2.8. However, if you are running Spark applications on EMR, you can use Spark built with Hadoop 2.7. -
7
HugeGraph
A graph database that supports more than 100+ billion data
HugeGraph is a convenient, efficient, and adaptable graph database compatible with the Apache TinkerPop3 framework and the Gremlin query language. HugeGraph supports fast import performance in the case of more than 10 billion Vertices and Edges Graph, millisecond-level OLTP query capability, and can be integrated into big data platforms like Hadoop or Spark for OLAP analysis. The main scenarios of HugeGraph include correlation search, fraud detection, and knowledge graph. Not only supports... -
8
Luigi
Python module that helps you build complex pipelines of batch jobs
Luigi is a Python (3.6, 3.7, 3.8, 3.9 tested) package that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more. The purpose of Luigi is to address all the plumbing typically associated with long-running batch processes. You want to chain many tasks, automate them, and failures will happen. These tasks can be anything, but are typically long running things like Hadoop... -
9
Apache Drill
Apache Drill is a distributed MPP query layer for self describing data
Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel. Get faster insights without the overhead (data loading, schema creation and maintenance, transformations, etc.) Analyze the multi-structured and nested data in non-relational datastores directly without transforming or restricting the data. Leverage your existing SQL skillsets and BI tools including Tableau... -
eloomi combines Learning Management (LMS), Onboarding, Authoring, and continuous Performance Management tools in a cloud-based solution. It allows companies to optimize skill training, onboarding and employee development with strong user experience to enhance productivity and employee satisfaction. As a white label solution, the platform can be customized to mirror a company's branding and logo.
-
10
Apache Hudi
Upserts, Deletes And Incremental Processing on Big Data
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). Apache Hudi is a transactional data lake platform that brings database and data warehouse capabilities to the data lake. Hudi reimagines slow old-school batch data processing with a powerful new incremental processing framework for low latency minute-level analytics. Hudi provides... -
11
Genie
Distributed Big Data Orchestration Service
Genie is a completely open source distributed 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. -
12
SeaweedFS
Distributed storage system for blobs, objects, files, and data lake
SeaweedFS is a distributed storage system for blobs, objects, files, and data lake, to store and serve billions of files fast! Blob store has O(1) disk seek, local tiering, cloud tiering. Filer supports cross-cluster active-active replication, Kubernetes, POSIX, S3 API, encryption, Erasure Coding for warm storage, FUSE mount, Hadoop, WebDAV. SeaweedFS is an independent Apache-licensed open source project with its ongoing development made possible because of the community. SeaweedFS is a simple... -
13
XGBoost
Scalable and Flexible Gradient Boosting
... can be used for Python, Java, Scala, R, C++ and more. It can run on a single machine, Hadoop, Spark, Dask, Flink and most other distributed environments, and is capable of solving problems beyond billions of examples. -
14
IoTDB
Apache IoTDB
Apache IoTDB (Database for Internet of Things) is an IoT native database with high performance for data management and analysis, deployable on the edge and the cloud. Due to its light-weight architecture, high performance and rich feature set together with its deep integration with Apache Hadoop, Spark and Flink, Apache IoTDB can meet the requirements of massive data storage, high-speed data ingestion and complex data analysis in the IoT industrial fields. In the scene of factories... -
15
Jupyter Enterprise Gateway
Enables Jupyter Notebooks to share resources across clusters
... that enables the ability to launch kernels on behalf of remote notebooks. This leads to better resource management, as the web server is no longer the single location for kernel activity. It essentially exposes a Kernel as a Service model. By default, the Jupyter framework runs kernels locally - potentially exhausting the server of resources. By leveraging the functionality of the underlying resource management applications like Hadoop YARN, Kubernetes, and others. -
16
TensorFlowOnSpark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters
By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. -
17
JRecord
Read Cobol data files in Java
provide Java Record based IO routines for Fixed Width (including Text, Mainframe, Cobol and Binary) and delimited Flat files via a Record Layout (Cobol, CSV or XML). The source is now available at https://github.com/bmTas/JRecord Projects using JRecord include: * https://github.com/thospfuller/rcoboldi - Cobol File in R * https://github.com/tmalaska/CopybookInputFormat - Cobol files in Hadoop * https://github.com/gss2002/copybook_formatter * https://github.com/gss2002/ftp2hdfs has some... -
18
OpenTSDB
A scalable, distributed time series database
OpenTSDB is a distributed, scalable Time Series Database (TSDB) written on top of HBase. OpenTSDB was written to address a common need: store, index and serve metrics collected from computer systems (network gear, operating systems, applications) at a large scale, and make this data easily accessible and graphable. Store and serve massive amounts of time series data without losing granularity. Generate graphs from the GUI, pull from the HTTP API, choose an open source front-end. OpenTSDB... -
19
spatial-framework-for-hadoop
The Spatial Framework for Hadoop allows developers
The Spatial Framework for Hadoop allows developers and data scientists to use the Hadoop data processing system for spatial data analysis. For tools, samples, and tutorials that use this framework, head over to GIS Tools for Hadoop. At the root level of this repository, you can build a single jar with everything in the framework using Apache Ant. Alternatively, you can build a jar at the root level of each framework component. Custom MapReduce jobs that use the Esri Geometry API require... -
20
geometry-api-java
The Esri Geometry API for Java enables developers to write apps
The Esri Geometry API for Java can be used to enable spatial data processing in 3rd-party data-processing solutions. Developers of custom MapReduce-based applications for Hadoop can use this API for spatial processing of data in the Hadoop system. The API is also used by the Hive UDF’s and could be used by developers building geometry functions for 3rd-party applications such as Cassandra, HBase, Storm and many other Java-based “big data” applications. -
21
Open Source Data Quality and Profiling
World's first open source data quality & data preparation project
..., Meta Data Discovery, Anomaly Discovery, Data Cleansing, Reporting and Analytic. It also had Hadoop ( Big data ) support to move files to/from Hadoop Grid, Create, Load and Profile Hive Tables. This project is also known as "Aggregate Profiler" Resful API for this project is getting built as (Beta Version) https://sourceforge.net/projects/restful-api-for-osdq/ apache spark based data quality is getting built at https://sourceforge.net/projects/apache-spark-osdq/ -
22
Custom Apache Big data Distribution
A Custom Apache Distribution including Spark and Hadoop, for Windows.
This Distribution has been customized to work out of the box. So, just download it, and unzip it. Set the Path variables for bin folders, HADOOP_HOME, SPARK_HOME, and JAVA_HOME. That's it..! use Hadoop and Spark natively on Windows. -
23
Oryx
Lambda architecture on Apache Spark, Apache Kafka for real-time
Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large-scale machine learning. It is a framework for building applications but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering. The application is written in Java, using Apache Spark, Hadoop, Tomcat, Kafka, Zookeeper and more. Configuration uses a single Typesafe Config config file, wherein... -
24
apache spark data pipeline osDQ
osDQ dedicated to create apache spark based data pipeline using JSON
... file Windows : java -cp .\lib\*;osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c .\example\samplerun.json Mac UNIX java -cp ./lib/*:./osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c ./example/samplerun.json For those on windows, you need to have hadoop distribtion unzipped on local drive and HADOOP_HOME set. Also copy winutils.exe from here into HADOOP_HOME\bin -
25