Alternatives to Trifacta logo

Alternatives to Trifacta

Tableau, OpenRefine, Talend, Power BI, and Apache Spark are the most popular alternatives and competitors to Trifacta.
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What is Trifacta and what are its top alternatives?

It is an Intelligent Platform that Interoperates with Your Data Investments. It sits between the data storage and processing environments and the visualization, statistical or machine learning tools used downstream
Trifacta is a tool in the Big Data Tools category of a tech stack.
Trifacta is an open source tool with GitHub stars and GitHub forks. Here’s a link to Trifacta's open source repository on GitHub

Top Alternatives to Trifacta

  • Tableau
    Tableau

    Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click. ...

  • OpenRefine
    OpenRefine

    It is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data. ...

  • Talend
    Talend

    It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms. ...

  • Power BI
    Power BI

    It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. ...

  • 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. ...

  • 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. ...

Trifacta alternatives & related posts

Tableau logo

Tableau

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Tableau helps people see and understand data.
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PROS OF TABLEAU
  • 4
    Capable of visualising billions of rows
  • 1
    Intuitive and easy to learn
  • 1
    Responsive
CONS OF TABLEAU
  • 1
    Very expensive for small companies

related Tableau posts

Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

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OpenRefine logo

OpenRefine

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Desktop application for data cleanup and transformation
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+ 1
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PROS OF OPENREFINE
    Be the first to leave a pro
    CONS OF OPENREFINE
      Be the first to leave a con

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      Talend logo

      Talend

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      A single, unified suite for all integration needs
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      PROS OF TALEND
        Be the first to leave a pro
        CONS OF TALEND
          Be the first to leave a con

          related Talend posts

          Power BI logo

          Power BI

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          Empower team members to discover insights hidden in your data
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          PROS OF POWER BI
          • 9
            Cross-filtering
          CONS OF POWER BI
            Be the first to leave a con

            related Power BI posts

            Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

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            Apache Spark logo

            Apache Spark

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            3K
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            Fast and general engine for large-scale data processing
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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

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            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 )

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            Splunk logo

            Splunk

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            Search, monitor, analyze and visualize machine data
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            PROS OF SPLUNK
            • 2
              Alert system based on custom query results
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              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

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            Shared insights
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            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.

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            Amazon Athena logo

            Amazon Athena

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            Query S3 Using SQL
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            PROS OF AMAZON ATHENA
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              Use SQL to analyze CSV files
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              Glue crawlers gives easy Data catalogue
            • 7
              Cheap
            • 5
              Query all my data without running servers 24x7
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              No data base servers yay
            • 3
              Easy integration with QuickSight
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              Query and analyse CSV,parquet,json files in sql
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              Also glue and athena use same data catalog
            • 1
              No configuration required
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              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.

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              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?

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              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
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                Unified batch and stream processing
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                Easy to use streaming apis
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                Out-of-the box connector to kinesis,s3,hdfs
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                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 · 580.6K views
                Shared insights
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                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.

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                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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