Sparkta alternatives and similar packages
Based on the "Big Data" category.
Alternatively, view Sparkta alternatives based on common mentions on social networks and blogs.
10.0 10.0 Sparkta VS Apache SparkApache Spark - A unified analytics engine for large-scale data processing
9.9 9.9 L1 Sparkta VS Deeplearning4JSuite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.
8.9 8.3 Sparkta VS Reactive-kafkaAlpakka Kafka connector - Alpakka is a Reactive Enterprise Integration library for Java and Scala, based on Reactive Streams and Akka.
7.0 8.4 Sparkta VS metorikkuA simplified, lightweight ETL Framework based on Apache Spark
3.5 0.0 Sparkta VS SchemerSchema registry for CSV, TSV, JSON, AVRO and Parquet schema. Supports schema inference and GraphQL API.
2.0 4.9 Sparkta VS GridScaleScala library for accessing various file, batch systems, job schedulers and grid middlewares.
1.9 0.0 Sparkta VS SparkplugSpark package to "plug" holes in data using SQL based rules ⚡️ 🔌
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of Sparkta or a related project?
After around two years of development, we have decided to discontinue this project due to a major refactor in its structure and in a near future we will launch Sparta 2.0.
We would like to thank all the open source community for their contribution. Needless to say that you can continue using this repository as a basis for your developments as it contains the latest stable version as of today and minor issues will be attended.
If you are interested in the new Sparta 2.0 with pipelines and workflows, please contact with us in the email [email protected]
About Stratio Sparta
At Stratio, we have implemented several real-time analytics projects based on Apache Spark, Kafka, Flume, Cassandra, ElasticSearch or MongoDB. These technologies were always a perfect fit, but soon we found ourselves writing the same pieces of integration code over and over again. Stratio Sparta is the easiest way to make use of the Apache Spark Streaming technology and all its ecosystem. Choose your input, operations and outputs, and start extracting insights out of your data in real-time.
- Pure Spark
- No need of coding, only declarative analytical workflows
- Data continuously streamed in & processed in near real-time
- Ready to use out-of-the-box
- Plug & play: flexible workflows (inputs, outputs, transformations, etc…)
- High performance and Fault Tolerance
- Scalable and High Availability
- Big Data OLAP on real-time to small data
- Triggers over streaming data
- Spark SQL language with streaming and batch data
- Kerberos and CAS compatible
Send one workflow as a JSON to Sparta API and execute in one Spark Cluster your own real-time plugins [Architecture](./images/architecture.jpg)
Sparta as a Job Manager
Send more than one Streaming Job in the Spark Cluster and manage them with a simple UI
Run workflows over Mesos, Yarn or SparkStandAlone
Sparta as a SDK
Modular components extensible with simple SDK
- You can extend several points of the platform to fulfill your needs, such as adding new inputs, outputs, operators, transformations.
- Add new functions to Kite SDK in order to extend the data cleaning, enrichment and normalization capabilities. [Architecture Detail](./images/architectureDetail.jpg)
On each workflow multiple components can be defined, but now all have the following architecture [workflow](./images/workflow.jpg) [Components](./images/components.jpg)
Several plugins are been implemented by Stratio Sparta team [Main plugins](./images/plugins.jpg)
With Sparta is possible to execute queries over the streaming data, execute ETL, aggregations and Simple Event Processing mixing streaming data with batch data on the trigger process. [triggers](./images/triggers.jpg)
The aggregation process in Sparta is very powerful because is possible to generate efficient OLAP processes with streaming data [OLAP](./images/OLAPintegration.jpg)
Advanced feature are been implemented in order to optimize the stateful operations over Spark Streaming [Aggregations](./images/aggregation.jpg)
- Http Rest
- Spark Streaming & Spark
- Apache Cassandra
- Apache Parquet
- Apache Kafka
- Apache Flume
- KiteSDK (morphlines)
- Apache Avro
Sparta provide several advantages to final Users [Advantages](./images/features.jpg)
You can generate rpm and deb packages by running:
mvn clean package -Ppackage
Note: you need to have installed the following programs in order to build these packages:
In a debian distribution:
In a centOS distribution:
In all distributions:
- Java 8
- Maven 3
Licensed to STRATIO (C) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The STRATIO (C) licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
*Note that all licence references and agreements mentioned in the Sparkta README section above are relevant to that project's source code only.