Google Cloud Dataproc vs Mule runtime engine

Overview

Mule runtime engine

Stacks127

Followers129

Votes8

Google Cloud Dataproc

Stacks33

Followers28

Votes0

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Detailed Comparison

Mule runtime engine	Google Cloud Dataproc
Its mission is to connect the world’s applications, data and devices. It makes connecting anything easy with Anypoint Platform™, the only complete integration platform for SaaS, SOA and APIs. Thousands of organizations in 60 countries, from emerging brands to Global 500 enterprises, use it to innovate faster and gain competitive advantage.	It is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. It helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them.
Connects data;Connects applications;Integration platform;Fast	Spin up an autoscaling cluster in 90 seconds on custom machines; Build fully managed Apache Spark, Apache Hadoop, Presto, and other OSS clusters; Only pay for the resources you use and lower the total cost of ownership of OSS; Encryption and unified security built into every cluster; Accelerate data science with purpose-built clusters
Statistics
Stacks 127	Stacks 33
Followers 129	Followers 28
Votes 8	Votes 0
Pros & Cons
Pros 4 Open Source 2 Microservices 2 Integration	No community feedback yet
Integrations
CloudApp API Umbrella Zapier	Hadoop Apache Spark Google Cloud Bigtable Google Cloud Storage Google BigQuery google-cloud-logging

What are some alternatives to Mule runtime engine, Google Cloud Dataproc?

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

Distributed SQL Query Engine for Big Data

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 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

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 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™ 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

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

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.