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Apache Flink
Apache Flink

141
149
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11
s3-lambda
s3-lambda

3
19
+ 1
0
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Apache Flink vs s3-lambda: What are the differences?

Apache Flink: 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; s3-lambda: Lambda functions over S3 objects: each, map, reduce, filter. s3-lambda enables you to run lambda functions over a context of S3 objects. It has a stateless architecture with concurrency control, allowing you to process a large number of files very quickly. This is useful for quickly prototyping complex data jobs without an infrastructure like Hadoop or Spark.

Apache Flink and s3-lambda can be primarily classified as "Big Data" tools.

Apache Flink and s3-lambda are both open source tools. It seems that Apache Flink with 9.11K GitHub stars and 4.86K forks on GitHub has more adoption than s3-lambda with 1.05K GitHub stars and 43 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 s3-lambda?

s3-lambda enables you to run lambda functions over a context of S3 objects. It has a stateless architecture with concurrency control, allowing you to process a large number of files very quickly. This is useful for quickly prototyping complex data jobs without an infrastructure like Hadoop or Spark.
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          What are some alternatives to Apache Flink and s3-lambda?
          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 s3-lambda
          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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