Need advice about which tool to choose?Ask the StackShare community!
Manage your open source components, licenses, and vulnerabilities
Learn MorePros of Apache Kudu
Pros of Apache Flink
Pros of Apache Spark
Pros of Apache Kudu
- Realtime Analytics10
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 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
Sign up to add or upvote prosMake informed product decisions
Cons of Apache Kudu
Cons of Apache Flink
Cons of Apache Spark
Cons of Apache Kudu
- Restart time1
Cons of Apache Flink
Be the first to leave a con
Cons of Apache Spark
- Speed4
Sign up to add or upvote consMake informed product decisions
45
133
1.9K
1
982
132
What is Apache Kudu?
A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.
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 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.
Need advice about which tool to choose?Ask the StackShare community!
What companies use Apache Kudu?
What companies use Apache Flink?
What companies use Apache Spark?
What companies use Apache Kudu?
What companies use Apache Flink?
What companies use Apache Spark?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Apache Kudu?
What tools integrate with Apache Flink?
What tools integrate with Apache Spark?
What tools integrate with Apache Kudu?
What tools integrate with Apache Flink?
What tools integrate with Apache Spark?
Sign up to get full access to all the tool integrationsMake informed product decisions
What are some alternatives to Apache Kudu, Apache Flink, and Apache Spark?
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
HBase
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
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 Impala
Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.