Google Cloud Dataproc vs ScratchDB

Overview

Google Cloud Dataproc

Stacks33

Followers28

Votes0

ScratchDB

Stacks64

Followers2

Votes0

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Detailed Comparison

Google Cloud Dataproc	ScratchDB
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.	It is an open-source alternative to BigQuery, Redshift, and Snowflake. It is a wrapper around Clickhouse that lets you input arbitrary JSON and perform analytical queries against it. It automatically creates tables and columns when new data is added.
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	Open source; Build analytics with zero infrastructure; Automatically create and optimize tables from JSON; Lets you stream data without a "CREATE TABLE"
Statistics
Stacks 33	Stacks 64
Followers 28	Followers 2
Votes 0	Votes 0
Integrations
Hadoop Apache Spark Google Cloud Bigtable Google Cloud Storage Google BigQuery google-cloud-logging	Clickhouse JSON

What are some alternatives to Google Cloud Dataproc, ScratchDB?

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.