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Apache Tez VS Spark Streaming

Compare Apache Tez VS Spark Streaming and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Apache Tez logo Apache Tez

Apache Tez is aimed at building an application framework which allows for a complex directed-acyclic-graph of tasks for processing data.

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
  • Apache Tez Landing page
    Landing page //
    2019-11-10
  • Spark Streaming Landing page
    Landing page //
    2022-01-10

Apache Tez videos

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Spark Streaming videos

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Spark Streaming Vs Kafka Streams || Which is The Best for Stream Processing?

More videos:

  • Tutorial - Spark Streaming Vs Structured Streaming Comparison | Big Data Hadoop Tutorial

Category Popularity

0-100% (relative to Apache Tez and Spark Streaming)

Apache Tez

Spark Streaming

Databases
100 100%
Databases
0% 0
Stream Processing
13 13%
Stream Processing
87% 87
Big Data
18 18%
Big Data
82% 82
Data Management
0 0%
Data Management
100% 100

User comments

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Social recommendations and mentions

Based on our record, Spark Streaming should be more popular than Apache Tez. It has been mentiond 3 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Tez mentions (1)

  • In One Minute : Hadoop
    Tez is an extensible framework for building high performance batch and interactive data processing applications, coordinated by YARN. - Source: dev.to / over 1 year ago

Spark Streaming mentions (3)

  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 4 months ago
  • Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
    Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / over 1 year ago
  • Spark for beginners - and you
    Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / over 2 years ago

What are some alternatives?

When comparing Apache Tez and Spark Streaming, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

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