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Open-source software for reliable, scalable, distributed computing
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What is Hadoop?

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Hadoop is a tool in the Databases category of a tech stack.
Hadoop is an open source tool with 12.8K GitHub stars and 7.9K GitHub forks. Here’s a link to Hadoop's open source repository on GitHub

Who uses Hadoop?

361 companies reportedly use Hadoop in their tech stacks, including Uber, Airbnb, and Shopify.

1650 developers on StackShare have stated that they use Hadoop.

Hadoop Integrations

Datadog, Azure Cosmos DB, Presto, Apache Flink, and Oracle PL/SQL are some of the popular tools that integrate with Hadoop. Here's a list of all 41 tools that integrate with Hadoop.
Pros of Hadoop
Great ecosystem
One stack to rule them all
Great load balancer
Amazon aws
Java syntax
Decisions about Hadoop

Here are some stack decisions, common use cases and reviews by companies and developers who chose Hadoop in their tech stack.

I am looking for the best tool to orchestrate #ETL workflows in non-Hadoop environments, mainly for regression testing use cases. Would Airflow or Apache NiFi be a good fit for this purpose?

For example, I want to run an Informatica ETL job and then run an SQL task as a dependency, followed by another task from Jira. What tool is best suited to set up such a pipeline?

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Shared insights

For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?

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Blog Posts

MySQLKafkaApache Spark+6
Aug 28 2019 at 3:10AM


PythonJavaAmazon S3+16

Jobs that mention Hadoop as a desired skillset

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Hadoop Alternatives & Comparisons

What are some alternatives to Hadoop?
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
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Hadoop's Followers
2144 developers follow Hadoop to keep up with related blogs and decisions.