Alternatives to Amazon Redshift Spectrum logo

Alternatives to Amazon Redshift Spectrum

Amazon Athena, Amazon Redshift, Apache Spark, Splunk, and Apache Flink are the most popular alternatives and competitors to Amazon Redshift Spectrum.
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What is Amazon Redshift Spectrum and what are its top alternatives?

With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
Amazon Redshift Spectrum is a tool in the Big Data Tools category of a tech stack.

Top Alternatives to Amazon Redshift Spectrum

  • 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. ...

  • Amazon Redshift
    Amazon Redshift

    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions. ...

  • 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. ...

  • Splunk
    Splunk

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

  • 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 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. ...

  • AWS Glue
    AWS Glue

    A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. ...

  • Presto
    Presto

    Distributed SQL Query Engine for Big Data

Amazon Redshift Spectrum alternatives & related posts

Amazon Athena logo

Amazon Athena

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707
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Query S3 Using SQL
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+ 1
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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
    Be the first to leave a con

    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
    Amazon Redshift logo

    Amazon Redshift

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    1.2K
    104
    Fast, fully managed, petabyte-scale data warehouse service
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    PROS OF AMAZON REDSHIFT
    • 37
      Data Warehousing
    • 27
      Scalable
    • 17
      SQL
    • 14
      Backed by Amazon
    • 5
      Encryption
    • 1
      Cheap and reliable
    • 1
      Isolation
    • 1
      Best Cloud DW Performance
    • 1
      Fast columnar storage
    CONS OF AMAZON REDSHIFT
      Be the first to leave a con

      related Amazon Redshift posts

      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 2.5M views

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

      See more
      Ankit Sobti

      Looker , Stitch , Amazon Redshift , dbt

      We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

      For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

      See more
      Apache Spark logo

      Apache Spark

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      Fast and general engine for large-scale data processing
      2.7K
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      PROS OF APACHE SPARK
      • 59
        Open-source
      • 48
        Fast and Flexible
      • 8
        One platform for every big data problem
      • 7
        Great for distributed SQL like applications
      • 6
        Easy to install and to use
      • 3
        Works well for most Datascience usecases
      • 2
        Interactive Query
      • 2
        In memory Computation
      • 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 · 2.3M 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 · 1.1M 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
      Splunk logo

      Splunk

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      13
      Search, monitor, analyze and visualize machine data
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      PROS OF SPLUNK
      • 2
        Alert system based on custom query results
      • 2
        API for searching logs, running reports
      • 2
        Query engine supports joining, aggregation, stats, etc
      • 1
        Ability to style search results into reports
      • 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
      Apache Flink logo

      Apache Flink

      450
      737
      36
      Fast and reliable large-scale data processing engine
      450
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      PROS OF APACHE FLINK
      • 15
        Unified batch and stream processing
      • 8
        Easy to use streaming apis
      • 8
        Out-of-the box connector to kinesis,s3,hdfs
      • 3
        Open Source
      • 2
        Low latency
      CONS OF APACHE FLINK
        Be the first to leave a con

        related Apache Flink posts

        Surabhi Bhawsar
        Technical Architect at Pepcus · | 7 upvotes · 589.3K 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?

        See more
        Apache Hive logo

        Apache Hive

        408
        413
        0
        Data Warehouse Software for Reading, Writing, and Managing Large Datasets
        408
        413
        + 1
        0
        PROS OF APACHE HIVE
          Be the first to leave a pro
          CONS OF APACHE HIVE
            Be the first to leave a con

            related Apache Hive posts

            Ashish Singh
            Tech Lead, Big Data Platform at Pinterest · | 37 upvotes · 1.1M 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
            AWS Glue logo

            AWS Glue

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            8
            Fully managed extract, transform, and load (ETL) service
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            8
            PROS OF AWS GLUE
            • 8
              Managed Hive Metastore
            CONS OF AWS GLUE
              Be the first to leave a con

              related AWS Glue posts

              Pardha Saradhi
              Technical Lead at Incred Financial Solutions · | 6 upvotes · 47.4K views

              Hi,

              We are currently storing the data in Amazon S3 using Apache Parquet format. We are using Presto to query the data from S3 and catalog it using AWS Glue catalog. We have Metabase sitting on top of Presto, where our reports are present. Currently, Presto is becoming too costly for us, and we are looking for alternatives for it but want to use the remaining setup (S3, Metabase) as much as possible. Please suggest alternative approaches.

              See more
              Punith Ganadinni
              Senior Product Engineer · | 2 upvotes · 39K views

              Hey all, I need some suggestions in creating a replica of our RDS DB for reporting and analytical purposes. Cost is a major factor. I was thinking of using AWS Glue to move data from Amazon RDS to Amazon S3 and use Amazon Athena to run queries on it. Any other suggestions would be appreciable.

              See more
              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
                Be the first to leave a con

                related Presto posts

                Ashish Singh
                Tech Lead, Big Data Platform at Pinterest · | 37 upvotes · 1.1M 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 · 2.3M 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