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  5. StreamSets vs Trifacta

StreamSets vs Trifacta

OverviewDecisionsComparisonAlternatives

Overview

Trifacta
Trifacta
Stacks19
Followers41
Votes0
StreamSets
StreamSets
Stacks53
Followers133
Votes0

StreamSets vs Trifacta: What are the differences?

StreamSets and Trifacta are both popular data integration tools used in the IT industry. Below are the key differences between StreamSets and Trifacta:

  1. User Interface: StreamSets offers a visually intuitive drag-and-drop interface, making it easy for users to design data pipelines without needing to write code. In contrast, Trifacta focuses on data wrangling and cleansing through a data grid interface, providing users with advanced data preparation capabilities.

  2. Focus: StreamSets primarily focuses on streamlining data movement and real-time processing tasks, enabling users to efficiently ingest and process large volumes of data. On the other hand, Trifacta is designed for data wrangling and cleaning tasks, emphasizing on data preparation for analytics and reporting purposes.

  3. Deployment: StreamSets is more suitable for building and deploying data pipelines in on-premises, cloud, or hybrid environments, offering flexibility in deploying data integration workflows. Trifacta, on the other hand, is often used in cloud-based environments for data preparation tasks, providing a scalable solution for handling diverse data sources.

  4. Data Transformation: StreamSets offers a wide range of pre-built connectors and processors for transforming data on-the-fly during ingestion, ensuring data quality and consistency. Trifacta, however, provides extensive data transformation capabilities through its visual interface, enabling users to clean, enrich, and reshape data for analytical purposes.

  5. Collaboration: StreamSets enables team collaboration on building data pipelines through shared repositories and version control, fostering teamwork and efficient development. Trifacta also supports collaboration features, allowing users to share data preparation workflows and collaborate on data cleaning tasks.

  6. Scalability: StreamSets is known for its scalability and performance in handling high-volume data processing tasks, suitable for enterprise-level data integration requirements. Trifacta, while scalable in cloud environments, is more focused on providing a user-friendly data preparation solution rather than large-scale data processing capabilities.

In Summary, StreamSets excels in real-time data movement and offers a user-friendly interface, while Trifacta specializes in data wrangling and preparation for analytics, with a focus on collaboration and scalability.

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Advice on Trifacta, StreamSets

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

Detailed Comparison

Trifacta
Trifacta
StreamSets
StreamSets

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

An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.

Interactive Exploration; Automated visual representations of data based upon its content in the most compelling visual profile; Predictive Transformation; Intelligent Execution; Collaborative Data Governance.
Only StreamSets provides a single design experience for all design patterns (batch, streaming, CDC, ETL, ELT, and ML pipelines) for 10x greater developer productivity; smart data pipelines that are resilient to change for 80% less breakages; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps.
Statistics
Stacks
19
Stacks
53
Followers
41
Followers
133
Votes
0
Votes
0
Pros & Cons
No community feedback yet
Cons
  • 2
    No user community
  • 1
    Crashes
Integrations
Microsoft Azure
Microsoft Azure
Google Cloud Storage
Google Cloud Storage
Snowflake
Snowflake
AWS Data Pipeline
AWS Data Pipeline
Tableau
Tableau
HBase
HBase
Databricks
Databricks
Amazon Redshift
Amazon Redshift
MySQL
MySQL
gRPC
gRPC
Google BigQuery
Google BigQuery
Amazon Kinesis
Amazon Kinesis
Cassandra
Cassandra
Hadoop
Hadoop
Redis
Redis

What are some alternatives to Trifacta, StreamSets?

Kafka

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

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.

ActiveMQ

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Presto

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

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