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  5. Apache Oozie vs Apache Spark

Apache Oozie vs Apache Spark

OverviewDecisionsComparisonAlternatives

Overview

Apache Oozie
Apache Oozie
Stacks40
Followers76
Votes0
Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K

Apache Oozie vs Apache Spark: What are the differences?

Introduction

In this article, we will compare Apache Oozie and Apache Spark, two popular frameworks used in big data processing. We will highlight the key differences between these two frameworks.

  1. Architecture: Apache Oozie is a workflow scheduler system used for managing Hadoop jobs. It provides a platform to define and execute workflows, which are sequences of Hadoop jobs. On the other hand, Apache Spark is an open-source, distributed computing system that provides a unified analytics engine for big data processing. It offers an in-memory computing model, allowing for faster data processing and iterative computations.

  2. Data Processing: Oozie focuses primarily on batch processing and is designed for large-scale data processing workflows. It excels in handling complex workflows involving multiple Hadoop jobs. Spark, on the other hand, can handle both batch and real-time data processing. It provides an interactive shell for real-time data exploration and supports stream processing through its Spark Streaming module.

  3. Programming Model: Oozie uses XML-based workflow definition language and requires developers to write code in various scripting languages such as Pig Latin, HiveQL, and MapReduce. It requires setting up and configuring multiple components for job execution. In contrast, Spark provides a rich set of APIs in Scala, Java, Python, and R, making it more developer-friendly. Spark programs are written in a unified API, providing a higher-level abstraction for building applications.

  4. Performance: Oozie workflows execute Hadoop jobs using MapReduce, which can be slower for iterative algorithms or interactive data exploration. Spark, on the other hand, uses its in-memory computing capability and DAG (Directed Acyclic Graph) execution model to achieve faster performance. It can cache intermediate data in memory, reducing the need for disk I/O and improving overall processing speed.

  5. Fault Tolerance: Oozie provides fault tolerance by tracking the execution status of individual workflow tasks and retrying failed tasks. It also supports job recovery in case of failures. Spark, on the other hand, offers fault tolerance through its Resilient Distributed Dataset (RDD) abstraction. RDDs automatically recover from node failures, allowing for reliable distributed processing.

  6. Ecosystem Integration: Oozie is tightly integrated with the Apache Hadoop ecosystem and provides support for various Hadoop components like MapReduce, Pig, Hive, and Sqoop. It can orchestrate complex workflows involving these components. Spark, on the other hand, has a broader ecosystem integration and supports integration with Hadoop, Hive, HBase, Cassandra, and other popular data sources. It can also be used in combination with other frameworks like Apache Kafka for real-time data processing.

In summary, Apache Oozie is a workflow scheduler system primarily focused on batch processing, while Apache Spark is a powerful analytics engine that supports batch and real-time data processing. Oozie relies on XML-based workflows and Hadoop jobs, whereas Spark offers a unified programming model and in-memory computing capabilities for faster performance.

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Advice on Apache Oozie, Apache Spark

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments

Detailed Comparison

Apache Oozie
Apache Oozie
Apache Spark
Apache Spark

It is a server-based workflow scheduling system to manage Hadoop jobs. Workflows in it are defined as a collection of control flow and action nodes in a directed acyclic graph. Control flow nodes define the beginning and the end of a workflow as well as a mechanism to control the workflow execution path.

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.

-
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars
-
GitHub Stars
42.2K
GitHub Forks
-
GitHub Forks
28.9K
Stacks
40
Stacks
3.1K
Followers
76
Followers
3.5K
Votes
0
Votes
140
Pros & Cons
No community feedback yet
Pros
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
Cons
  • 4
    Speed

What are some alternatives to Apache Oozie, Apache Spark?

Airflow

Airflow

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

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.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

GitHub Actions

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

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.

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