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Apache Flink
Apache Flink

141
149
+ 1
11
Vespa
Vespa

4
12
+ 1
0
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Apache Flink vs Vespa: What are the differences?

Developers describe Apache Flink as "Fast and reliable large-scale data processing engine". 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. On the other hand, Vespa is detailed as "Store, search, rank and organize big data". Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.

Apache Flink and Vespa can be categorized as "Big Data" tools.

Apache Flink and Vespa are both open source tools. Apache Flink with 9.35K GitHub stars and 5K forks on GitHub appears to be more popular than Vespa with 2.85K GitHub stars and 339 GitHub forks.

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 Vespa?

Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.
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        What are some alternatives to Apache Flink and Vespa?
        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.
        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.
        Beam
        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.
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        How developers use Apache Flink and Vespa
        Avatar of Coolfront Technologies
        Coolfront Technologies uses Apache FlinkApache Flink

        Used for analytics on streaming data.

        Avatar of rmetzger
        rmetzger uses Apache FlinkApache Flink

        Flink for stream data analytics

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