Alternatives to Apache Impala logo

Alternatives to Apache Impala

Presto, Apache Drill, Apache Hive, Apache Spark, and HBase are the most popular alternatives and competitors to Apache Impala.
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What is Apache Impala and what are its top alternatives?

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 Impala is a tool in the Big Data Tools category of a tech stack.
Apache Impala is an open source tool with 27 GitHub stars and 29 GitHub forks. Here’s a link to Apache Impala's open source repository on GitHub

Top Alternatives to Apache Impala

  • Presto
    Presto

    Distributed SQL Query Engine for Big Data

  • Apache Drill
    Apache Drill

    Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel. ...

  • Apache Hive
    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 Spark
    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. ...

  • HBase
    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. ...

  • Splunk
    Splunk

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

  • Amazon Athena
    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

    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. ...

Apache Impala alternatives & related posts

Presto logo

Presto

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Distributed SQL Query Engine for Big Data
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PROS OF PRESTO
  • 18
    Works directly on files in s3 (no ETL)
  • 13
    Open-source
  • 12
    Join multiple databases
  • 10
    Scalable
  • 7
    Gets ready in minutes
  • 6
    MPP
CONS OF PRESTO
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    related Presto posts

    Ashish Singh
    Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 1.3M 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

    See more
    Eric Colson
    Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.6M views

    The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

    Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

    At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

    For more info:

    #DataScience #DataStack #Data

    See more
    Apache Drill logo

    Apache Drill

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    Schema-Free SQL Query Engine for Hadoop and NoSQL
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    PROS OF APACHE DRILL
    • 4
      NoSQL and Hadoop
    • 3
      Free
    • 3
      Lightning speed and simplicity in face of data jungle
    • 2
      Well documented for fast install
    • 1
      SQL interface to multiple datasources
    • 1
      Nested Data support
    • 1
      Read Structured and unstructured data
    • 1
      V1.10 released - https://drill.apache.org/
    CONS OF APACHE DRILL
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      related Apache Drill posts

      Apache Hive logo

      Apache Hive

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      Data Warehouse Software for Reading, Writing, and Managing Large Datasets
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      PROS OF APACHE HIVE
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        CONS OF APACHE HIVE
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          related Apache Hive posts

          Ashish Singh
          Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 1.3M 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

          See more
          Jan Vlnas
          Developer Advocate at Superface · | 5 upvotes · 28.5K views

          From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.

          I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.

          Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.

          See more
          Apache Spark logo

          Apache Spark

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          Fast and general engine for large-scale data processing
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          PROS OF APACHE SPARK
          • 60
            Open-source
          • 48
            Fast and Flexible
          • 8
            Great for distributed SQL like applications
          • 8
            One platform for every big data problem
          • 6
            Easy to install and to use
          • 3
            Works well for most Datascience usecases
          • 2
            In memory Computation
          • 2
            Interactive Query
          • 2
            Machine learning libratimery, Streaming in real
          CONS OF APACHE SPARK
          • 3
            Speed

          related Apache Spark posts

          Eric Colson
          Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.6M views

          The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

          Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

          At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

          For more info:

          #DataScience #DataStack #Data

          See more
          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1.3M views

          Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

          Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

          https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

          (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

          See more
          HBase logo

          HBase

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          The Hadoop database, a distributed, scalable, big data store
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          PROS OF HBASE
          • 9
            Performance
          • 5
            OLTP
          • 1
            Fast Point Queries
          CONS OF HBASE
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            related HBase posts

            I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!

            See more

            Hi, I'm building a machine learning pipelines to store image bytes and image vectors in the backend.

            So, when users query for the random access image data (key), we return the image bytes and perform machine learning model operations on it.

            I'm currently considering going with Amazon S3 (in the future, maybe add Redis caching layer) as the backend system to store the information (s3 buckets with sharded prefixes).

            As the latency of S3 is 100-200ms (get/put) and it has a high throughput of 3500 puts/sec and 5500 gets/sec for a given bucker/prefix. In the future I need to reduce the latency, I can add Redis cache.

            Also, s3 costs are way fewer than HBase (on Amazon EC2 instances with 3x replication factor)

            I have not personally used HBase before, so can someone help me if I'm making the right choice here? I'm not aware of Hbase latencies and I have learned that the MOB feature on Hbase has to be turned on if we have store image bytes on of the column families as the avg image bytes are 240Kb.

            See more
            Splunk logo

            Splunk

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            Search, monitor, analyze and visualize machine data
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            PROS OF SPLUNK
            • 2
              Ability to style search results into reports
            • 2
              Alert system based on custom query results
            • 2
              API for searching logs, running reports
            • 2
              Query engine supports joining, aggregation, stats, etc
            • 1
              Query any log as key-value pairs
            • 1
              Splunk language supports string, date manip, math, etc
            • 1
              Granular scheduling and time window support
            • 1
              Custom log parsing as well as automatic parsing
            • 1
              Dashboarding on any log contents
            • 1
              Rich GUI for searching live logs
            CONS OF SPLUNK
            • 1
              Splunk query language rich so lots to learn

            related Splunk posts

            Shared insights
            on
            KibanaKibanaSplunkSplunkGrafanaGrafana

            I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

            See more
            Shared insights
            on
            SplunkSplunkElasticsearchElasticsearch

            We are currently exploring Elasticsearch and Splunk for our centralized logging solution. I need some feedback about these two tools. We expect our logs in the range of upwards > of 10TB of logging data.

            See more
            Amazon Athena logo

            Amazon Athena

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            Query S3 Using SQL
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            PROS OF AMAZON ATHENA
            • 15
              Use SQL to analyze CSV files
            • 8
              Glue crawlers gives easy Data catalogue
            • 7
              Cheap
            • 5
              Query all my data without running servers 24x7
            • 4
              No data base servers yay
            • 3
              Easy integration with QuickSight
            • 2
              Query and analyse CSV,parquet,json files in sql
            • 2
              Also glue and athena use same data catalog
            • 1
              No configuration required
            • 0
              Ad hoc checks on data made easy
            CONS OF AMAZON ATHENA
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              related Amazon Athena posts

              I use Amazon Athena because similar to Google BigQuery , you can store and query data easily. Especially since you can define data schema in the Glue data catalog, there's a central way to define data models.

              However, I would not recommend for batch jobs. I typically use this to check intermediary datasets in data engineering workloads. It's good for getting a look and feel of the data along its ETL journey.

              See more

              Hi all,

              Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

              See more
              Apache Flink logo

              Apache Flink

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              Fast and reliable large-scale data processing engine
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              PROS OF APACHE FLINK
              • 16
                Unified batch and stream processing
              • 8
                Easy to use streaming apis
              • 8
                Out-of-the box connector to kinesis,s3,hdfs
              • 4
                Open Source
              • 2
                Low latency
              CONS OF APACHE FLINK
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                related Apache Flink posts

                Surabhi Bhawsar
                Technical Architect at Pepcus · | 7 upvotes · 629.3K views
                Shared insights
                on
                KafkaKafkaApache FlinkApache Flink

                I need to build the Alert & Notification framework with the use of a scheduled program. We will analyze the events from the database table and filter events that are falling under a day timespan and send these event messages over email. Currently, we are using Kafka Pub/Sub for messaging. The customer wants us to move on Apache Flink, I am trying to understand how Apache Flink could be fit better for us.

                See more

                I have to build a data processing application with an Apache Beam stack and Apache Flink runner on an Amazon EMR cluster. I saw some instability with the process and EMR clusters that keep going down. Here, the Apache Beam application gets inputs from Kafka and sends the accumulative data streams to another Kafka topic. Any advice on how to make the process more stable?

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