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  5. Talend vs Trifacta

Talend vs Trifacta

OverviewDecisionsComparisonAlternatives

Overview

Talend
Talend
Stacks297
Followers249
Votes0
Trifacta
Trifacta
Stacks19
Followers41
Votes0

Talend vs Trifacta: What are the differences?

Introduction

This Markdown code provides a comparison between Talend and Trifacta, highlighting their key differences.

  1. Data Integration Capabilities: Talend is a powerful data integration platform that offers extensive features for designing and running data integration jobs. It provides a wide range of connectors, transformations, and scheduling options, making it highly suitable for complex data integration tasks. Trifacta, on the other hand, focuses more on data wrangling and preparation, offering a user-friendly interface for visually exploring and transforming data. While Trifacta also supports some integration capabilities, it is primarily designed for self-service data preparation.

  2. User Interface: Talend provides a comprehensive desktop-based graphical interface that allows users to visually design and configure data integration and transformation processes. It offers a drag-and-drop interface with a large library of pre-built components, making it easier for users to define their workflows. Trifacta, on the other hand, offers a web-based interface that is highly interactive and intuitive. It provides a visual representation of data and offers various tools for data exploration and transformation.

  3. Collaboration and Governance: Talend offers robust collaboration and governance features, allowing multiple developers to work together on the same projects. It provides features like version control, role-based access control, and project sharing, ensuring effective collaboration and governance in data integration projects. Trifacta, on the other hand, mainly focuses on individual data wrangling tasks and lacks extensive collaboration and governance features. It is more suitable for ad-hoc data preparation and exploration tasks rather than large-scale collaborative projects.

  4. Data Profiling and Quality: Talend comes with built-in data profiling and quality features, allowing users to analyze and identify data issues, such as missing values, duplicates, and inconsistencies. It provides a range of statistical and data quality indicators to assess the overall quality of data. Trifacta, although it offers some basic data profiling capabilities, does not provide as extensive data quality features as Talend.

  5. Deployment Options: Talend offers a variety of deployment options to meet different business needs. It supports on-premises, cloud, and hybrid deployments, giving users flexibility in choosing the deployment model that best suits their requirements. Trifacta, on the other hand, primarily focuses on cloud-based deployments and offers limited on-premises options. It is designed to take advantage of the scalability and flexibility of cloud infrastructures.

  6. Machine Learning Integration: Talend provides integration with popular machine learning and advanced analytics frameworks, allowing users to build predictive models and perform advanced data analysis. It offers pre-built machine learning components and connectors for seamless integration with frameworks like Apache Spark and Hadoop. Trifacta, although it supports some advanced analytics capabilities, does not offer the same level of integration with machine learning frameworks as Talend.

In summary, Talend is a powerful data integration platform with extensive capabilities for designing and running complex data integration workflows. It offers rich collaboration features, advanced data profiling, and quality capabilities, along with flexible deployment options. Trifacta, on the other hand, focuses more on self-service data preparation and exploration, providing a user-friendly interface for visually transforming and exploring data. While it offers some integration features, it lacks advanced collaboration, deployment options, and machine learning integration capabilities.

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Advice on Talend, 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
karunakaran
karunakaran

Consultant

Jun 26, 2020

Needs advice

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

  1. Create pipelines to ingest the data from multiple sources into the data lake
  2. Help me in aggregating and filtering data available in the data lake.
  3. Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

80.4k views80.4k
Comments

Detailed Comparison

Talend
Talend
Trifacta
Trifacta

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.

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

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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
297
Stacks
19
Followers
249
Followers
41
Votes
0
Votes
0
Integrations
No integrations available
Microsoft Azure
Microsoft Azure
Google Cloud Storage
Google Cloud Storage
Snowflake
Snowflake
AWS Data Pipeline
AWS Data Pipeline
Tableau
Tableau

What are some alternatives to Talend, Trifacta?

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.

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.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

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

Apache Impala

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.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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