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Apache Hive

Data Warehouse Software for Reading, Writing, and Managing Large Datasets
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What is Apache Hive?

Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
Apache Hive is a tool in the Big Data Tools category of a tech stack.
Apache Hive is an open source tool with 5.4K GitHub stars and 4.6K GitHub forks. Here’s a link to Apache Hive's open source repository on GitHub

Who uses Apache Hive?

68 companies reportedly use Apache Hive in their tech stacks, including Delivery Hero, Hepsiburada, and Walmart.

361 developers on StackShare have stated that they use Apache Hive.

Apache Hive Integrations

Apache Spark, Hadoop, DBeaver, HBase, and Apache NiFi are some of the popular tools that integrate with Apache Hive. Here's a list of all 22 tools that integrate with Apache Hive.
Decisions about Apache Hive

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

Shehryar Mallick
Associate Data Engineer at Virtuosoft · | 5 upvotes · 19.4K views
Needs advice
Apache HiveApache Hive

I've been going over the documentation and couldn't find answers to different questions like:

Apache Hive is built on top of Hadoop meaning if I wanted to scale it up I could do either horizontal scaling or vertical scaling. but if I want to scale up openrefine to cater more data then how can this be achieved? the only thing I could find was to allocate more memory like 2 of 4GB but using this approach would mean that we would run out of memory to allot. so thoughts on this?

Secondly, Hadoop has MapReduce meaning a task is reduced to many mapper running in parallel to perform the task which in turn increase the processing speed, is there a similar mechanism in OpenRefine or does it only have a single processing unit (as it is running locally). thoughts?

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Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

#BigData #AWS #DataScience #DataEngineering

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Blog Posts

Apache Hive's Features

  • Built on top of Apache Hadoop
  • Tools to enable easy access to data via SQL
  • Support for extract/transform/load (ETL), reporting, and data analysis
  • Access to files stored either directly in Apache HDFS and HBase
  • Query execution using Apache Hadoop MapReduce, Tez or Spark frameworks

Apache Hive Alternatives & Comparisons

What are some alternatives to Apache Hive?
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
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.
Distributed SQL Query Engine for Big Data
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.
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.
See all alternatives

Apache Hive's Followers
473 developers follow Apache Hive to keep up with related blogs and decisions.