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  5. Trifacta vs s3-lambda

Trifacta vs s3-lambda

OverviewDecisionsComparisonAlternatives

Overview

Trifacta
Trifacta
Stacks19
Followers41
Votes0
s3-lambda
s3-lambda
Stacks4
Followers64
Votes0
GitHub Stars1.1K
Forks47

Trifacta vs s3-lambda: What are the differences?

Introduction:

Trifacta and s3-lambda are two different tools that are used for data processing and management. In this analysis, we will explore the key differences between Trifacta and s3-lambda.

  1. Data Transformation Functionality: Trifacta excels in providing a comprehensive set of data transformation capabilities. It offers a rich set of built-in functions, data wrangling tools, and visual transformations, making it easy to clean and prepare data for analysis. On the other hand, s3-lambda is primarily focused on serverless computing and does not offer as robust data transformation features as Trifacta.

  2. Deployment and Scalability: Trifacta is a cloud-based data preparation tool that allows users to leverage the scalability and flexibility of cloud computing resources. It can handle large volumes of data and provides seamless integration with popular cloud platforms like AWS, Google Cloud, and Azure. s3-lambda, on the other hand, is specifically designed for serverless computing and data processing on AWS S3. It provides easy deployment and scalability options by leveraging AWS Lambda functions.

  3. User Interface and Ease of Use: Trifacta offers a user-friendly and intuitive interface that simplifies the data preparation process. Its visual transformation capabilities allow users to interactively explore and clean data through a drag-and-drop interface. s3-lambda, being a serverless computing tool, does not offer a visual interface. It requires users to write code and configure AWS Lambda functions for data processing, which can be more complex and less user-friendly for non-technical users.

  4. Integration with Ecosystem: Trifacta integrates well with various data storage systems, databases, and analytical tools. It supports direct connections with sources like AWS S3, Google BigQuery, and many more. Additionally, it provides connectors to popular BI and data visualization tools like Tableau and Power BI. s3-lambda, on the other hand, is tightly integrated with the AWS ecosystem, specifically AWS S3. It can seamlessly process data stored in S3 buckets using Lambda functions.

  5. Collaboration and Sharing: Trifacta offers collaborative features that enable teams to work together on data preparation tasks. It allows users to share and collaborate on data transformations, making it easier to collaborate on data projects. s3-lambda, being a serverless tool, does not provide built-in collaboration features. However, it can be utilized in conjunction with other AWS services like AWS Glue and AWS Athena to enable collaboration and sharing of data processing workflows.

  6. Pricing Model: Trifacta follows a subscription-based pricing model, where users pay based on the features and level of usage. The pricing is structured according to the number of users, volume of processed data, and additional add-ons. s3-lambda, being an AWS service, follows a pay-as-you-go pricing model. Users are charged based on the number of Lambda invocations, execution duration, and the amount of data processed. The pricing is more granular and flexible, allowing users to optimize costs based on their specific data processing needs.

In Summary, Trifacta provides a comprehensive set of data transformation capabilities with a user-friendly interface, while s3-lambda focuses on serverless computing and easy integration within the AWS ecosystem. Trifacta allows for easier collaboration and integration with various data sources and analytical tools, while s3-lambda provides seamless scalability and deployment options on Amazon S3. The two tools also have different pricing models, with Trifacta following a subscription-based model and s3-lambda adopting a pay-as-you-go approach.

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Advice on Trifacta, s3-lambda

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
s3-lambda
s3-lambda

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

s3-lambda enables you to run lambda functions over a context of S3 objects. It has a stateless architecture with concurrency control, allowing you to process a large number of files very quickly. This is useful for quickly prototyping complex data jobs without an infrastructure like Hadoop or Spark.

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
GitHub Stars
-
GitHub Stars
1.1K
GitHub Forks
-
GitHub Forks
47
Stacks
19
Stacks
4
Followers
41
Followers
64
Votes
0
Votes
0
Integrations
Microsoft Azure
Microsoft Azure
Google Cloud Storage
Google Cloud Storage
Snowflake
Snowflake
AWS Data Pipeline
AWS Data Pipeline
Tableau
Tableau
Amazon S3
Amazon S3
AWS Lambda
AWS Lambda

What are some alternatives to Trifacta, s3-lambda?

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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