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Apache Hive
Apache Hive

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Apache Impala
Apache Impala

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Apache Hive vs Apache Impala: What are the differences?

Developers describe Apache Hive as "Data Warehouse Software for Reading, Writing, and Managing Large Datasets". Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. On the other hand, Apache Impala is detailed as "Real-time Query for Hadoop". 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.

Apache Hive and Apache Impala can be primarily classified as "Big Data" tools.

Some of the features offered by Apache Hive are:

  • 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

On the other hand, Apache Impala provides the following key features:

  • Do BI-style Queries on Hadoop
  • Unify Your Infrastructure
  • Implement Quickly

Apache Hive and Apache Impala are both open source tools. It seems that Apache Hive with 2.68K GitHub stars and 2.63K forks on GitHub has more adoption than Apache Impala with 2.19K GitHub stars and 825 GitHub forks.

According to the StackShare community, Apache Hive has a broader approval, being mentioned in 36 company stacks & 42 developers stacks; compared to Apache Impala, which is listed in 17 company stacks and 37 developer stacks.

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.

What is 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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        What are some alternatives to Apache Hive and Apache Impala?
        HBase
        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.
        Presto
        Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
        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.
        See all alternatives
        Decisions about Apache Hive and Apache Impala
        Ashish Singh
        Ashish Singh
        Tech Lead, Big Data Platform at Pinterest · | 20 upvotes · 34.9K views
        Apache Hive
        Apache Hive
        Kubernetes
        Kubernetes
        Kafka
        Kafka
        Amazon S3
        Amazon S3
        Amazon EC2
        Amazon EC2
        Presto
        Presto
        #DataScience
        #DataEngineering
        #AWS
        #BigData

        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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