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Learn MorePros of Apache Flink
Pros of Mara
Pros of Apache Spark
Pros of Apache Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
Pros of Mara
- Great developing experience1
- ETL Tool1
- UI focused on ETL development1
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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Cons of Apache Flink
Cons of Mara
Cons of Apache Spark
Cons of Apache Flink
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Cons of Mara
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Cons of Apache Spark
- Speed4
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What is Apache Flink?
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
What is Mara?
A lightweight ETL framework with a focus on transparency and complexity reduction.
What is Apache Spark?
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
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Jobs that mention Apache Flink, Mara, and Apache Spark as a desired skillset
What companies use Apache Flink?
What companies use Mara?
What companies use Apache Spark?
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What companies use Mara?
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What tools integrate with Apache Flink?
What tools integrate with Mara?
What tools integrate with Apache Spark?
What tools integrate with Apache Flink?
What tools integrate with Mara?
No integrations found
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What are some alternatives to Apache Flink, Mara, and Apache Spark?
Apache Storm
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
Akutan
A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
Apache Flume
It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Kafka Streams
It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.