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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Azure Data Factory vs Mondrian

Azure Data Factory vs Mondrian

OverviewDecisionsComparisonAlternatives

Overview

Mondrian
Mondrian
Stacks19
Followers26
Votes0
GitHub Stars1.2K
Forks735
Azure Data Factory
Azure Data Factory
Stacks253
Followers484
Votes0
GitHub Stars516
Forks610

Mondrian vs Azure Data Factory: What are the differences?

Developers describe Mondrian as "Open source platform for big data deployments". It is a Hitachi Group Company, data integration and business analytics company with an enterprise, Online Analytical Processing server (OLAP). Allows business users to analyze large and complex amounts of data in real-time. On the other hand, Azure Data Factory is detailed as "Create, Schedule, & Manage Data Pipelines". It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

Mondrian and Azure Data Factory can be categorized as "Big Data" tools.

Some of the features offered by Mondrian are:

  • Analyze all your data in real-time
  • System responds to queries fast enough to allow an interactive exploration of the data
  • Brings multidimensional analysis to the masses

On the other hand, Azure Data Factory provides the following key features:

  • Real-Time Integration
  • Parallel Processing
  • Data Chunker

Mondrian and Azure Data Factory are both open source tools. Mondrian with 814 GitHub stars and 617 forks on GitHub appears to be more popular than Azure Data Factory with 150 GitHub stars and 255 GitHub forks.

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Advice on Mondrian, Azure Data Factory

Vamshi
Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceonPySparkPySparkAzure Data FactoryAzure Data FactoryDatabricksDatabricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

269k views269k
Comments

Detailed Comparison

Mondrian
Mondrian
Azure Data Factory
Azure Data Factory

It is a Hitachi Group Company, data integration and business analytics company with an enterprise, Online Analytical Processing server (OLAP). Allows business users to analyze large and complex amounts of data in real-time.

It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

Analyze all your data in real-time; System responds to queries fast enough to allow an interactive exploration of the data; Brings multidimensional analysis to the masses; Allowing users to examine business data by drilling and cross-tabulating information.
Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
Statistics
GitHub Stars
1.2K
GitHub Stars
516
GitHub Forks
735
GitHub Forks
610
Stacks
19
Stacks
253
Followers
26
Followers
484
Votes
0
Votes
0
Integrations
JavaScript
JavaScript
Java
Java
Ubuntu
Ubuntu
Octotree
Octotree
Java
Java
.NET
.NET

What are some alternatives to Mondrian, Azure Data Factory?

Apache Spark

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.

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.

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.

Apache Camel

Apache Camel

An open source Java framework that focuses on making integration easier and more accessible to developers.

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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