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  5. Tableau vs Trifacta

Tableau vs Trifacta

OverviewDecisionsComparisonAlternatives

Overview

Tableau
Tableau
Stacks1.3K
Followers1.4K
Votes8
Trifacta
Trifacta
Stacks19
Followers41
Votes0

Tableau vs Trifacta: What are the differences?

Introduction

In the realm of data analytics and visualization tools, Tableau and Trifacta are two popular platforms that help users derive insights from their data. However, they differ in various aspects, making each suitable for different use cases.

  1. User Interface: Tableau boasts a user-friendly interface with drag-and-drop functionality, making it easy for non-technical users to create visualizations. In contrast, Trifacta focuses more on data preparation and cleansing, offering a visually appealing interface that is optimized for data wrangling tasks.

  2. Data Processing: Tableau primarily focuses on data visualization, enabling users to create interactive and dynamic dashboards. On the other hand, Trifacta excels in data transformation, allowing users to clean, reshape, and enrich data before visualizing it in other tools.

  3. Collaboration and Sharing: Tableau offers robust sharing and collaboration features, allowing users to publish interactive dashboards and reports online. In comparison, Trifacta simplifies data sharing within the platform, enabling teams to collaborate on data cleaning and preparation tasks.

  4. Data Sources Integration: Tableau supports a wide range of data sources, including databases, cloud storages, and spreadsheets, making it versatile for various data formats. Trifacta is specifically designed for handling diverse data sources, offering connectors for structured and unstructured data sources.

  5. Automation and Scalability: Tableau provides automation features through Tableau Prep Builder but may require additional tools for scalability in enterprise settings. Trifacta emphasizes automated data preparation processes and scales efficiently in enterprise environments, catering to large datasets and complex workflows.

  6. Cost and Licensing: Tableau offers different pricing tiers based on usage and deployment options, catering to individual users, small businesses, and enterprises. Trifacta also offers flexible licensing models, with options for cloud deployment and enterprise-grade solutions tailored to specific business needs.

In Summary, the key differences between Tableau and Trifacta lie in their user interfaces, data processing capabilities, collaboration features, data source integration, automation, scalability, and cost structures.

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Advice on Tableau, Trifacta

Sarah
Sarah

Jun 25, 2020

Needs adviceonOpenRefineOpenRefine

I'm looking for an open-source/free/cheap tool to clean messy data coming from various travel APIs. We use many different APIs and save the info in our DB. However, many duplicates cannot be easily recognized as such.

We would either write an algorithm or use smart technology/tools with ML to help with product management.

While there are many things to be considered, this is one feature that it should have:

"To avoid confusion, we need to merge the suppliers & products accordingly. Products and suppliers must be able to be merged and assigned separately.

Reason: It may happen that one supplier offers different products. E.g., 1 tour operator offers 3 products via 1 API, but only 1 product with 3 (or a different amount of) variations via a different API. Also, the commission may differ for products, which we need to consider. Very often, products that are live (are bookable in real-time) on via 1 API, but are not live on the other. E.g., Supplier product 1 & 2 of API1 are live, product 3 not. For the same supplier, API2 provides live availability for products 1, 2, and 3.

Summing up, when merging the suppliers (tour operators) we need to consider:

  • Are the products the same for all APIs?
  • Which booking system API gives a better commission? Note: Some APIs charge us 1-5% depending on the monthly sale, which needs to be considered
  • Which booking system provides live availability
  • Is it the same supplier, or is the name only similar?

Most of the time, the supplier names differ even if they are the same (e.g., API1 often names them XX Pty Ltd, while API2 leaves "Pty Ltd" out). Additionally, the product title, description, etc. differ.

We need to write logic and create an algorithm to find the duplicates & to merge, assign, or (de)activate the respective supplier or product. My previous developer started a module to merge the suppliers, which does not seem to work correctly. Also, it is way too time taking considering the high amount of products that we have.

I would recommend merging, assigning etc. products and suppliers only if our algorithm says it's 90- 100% the matching supplier/product. Otherwise, admins need to be able to check & modify this. E.g. everything with a lower possibility of matching will be matched automatically, but can be undone or modified.

The next time the cron job runs, this needs to be considered to avoid recreating duplicates & creating a mess."

I am not sure in what way OpenRefine can help to achieve this and what ML tool can be connected to learn from the decisions the product management team makes. Maybe you have an idea of how other travel portals deal with messy data, duplicates, etc.?

I'm looking for the cheapest solution for a start-up, but it should do the work properly.

19.2k views19.2k
Comments
Vojtech
Vojtech

Head of Data at Mews

Nov 24, 2019

Decided

Power BI is really easy to start with. If you have just several Excel sheets or CSV files, or you build your first automated pipeline, it is actually quite intuitive to build your first reports.

And as we have kept growing, all the additional features and tools were just there within the Azure platform and/or Office 365.

Since we started building Mews, we have already passed several milestones in becoming start up, later also a scale up company and now getting ready to grow even further, and during all these phases Power BI was just the right tool for us.

353k views353k
Comments
Wei
Wei

CTO at Flux Work

Jan 8, 2020

Decided

Very easy-to-use UI. Good way to make data available inside the company for analysis.

Has some built-in visualizations and can be easily integrated with other JS visualization libraries such as D3.

Can be embedded into product to provide reporting functions.

Support team are helpful.

The only complain I have is lack of API support. Hard to track changes as codes and automate report deployment.

230k views230k
Comments

Detailed Comparison

Tableau
Tableau
Trifacta
Trifacta

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.

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

Connect to data on prem or in the cloud—whether it’s big data, a SQL database, a spreadsheet, or cloud apps like Google Analytics and Salesforce. Access and combine disparate data without writing code. Power users can pivot, split, and manage metadata to optimize data sources. Analysis begins with data. Get more from yours with Tableau.; Exceptional analytics demand more than a pretty dashboard. Quickly build powerful calculations from existing data, drag and drop reference lines and forecasts, and review statistical summaries. Make your point with trend analyses, regressions, and correlations for tried and true statistical understanding. Ask new questions, spot trends, identify opportunities, and make data-driven decisions with confidence.; Answer the “where” as well as the “why.” Create interactive maps automatically. Built-in postal codes mean lightning-fast mapping for more than 50 countries worldwide. Use custom geocodes and territories for personalized regions, like sales areas. We designed Tableau maps specifically to help your data stand out.; Ditch the static slides for live stories that others can explore. Create a compelling narrative that empowers everyone you work with to ask their own questions, analyzing interactive visualizations with fresh data. Be part of a culture of data collaboration, extending the impact of your insights.
Interactive Exploration; Automated visual representations of data based upon its content in the most compelling visual profile; Predictive Transformation; Intelligent Execution; Collaborative Data Governance.
Statistics
Stacks
1.3K
Stacks
19
Followers
1.4K
Followers
41
Votes
8
Votes
0
Pros & Cons
Pros
  • 6
    Capable of visualising billions of rows
  • 1
    Responsive
  • 1
    Intuitive and easy to learn
Cons
  • 3
    Very expensive for small companies
No community feedback yet
Integrations
No integrations available
Microsoft Azure
Microsoft Azure
Google Cloud Storage
Google Cloud Storage
Snowflake
Snowflake
AWS Data Pipeline
AWS Data Pipeline

What are some alternatives to Tableau, Trifacta?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

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.

Presto

Presto

Distributed SQL Query Engine for Big 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.

Superset

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

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.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Cube

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

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.

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