AWS re:Invent is in full swing at the moment. As an AWS certified Machine Learning Competency and Data & Analytics Competency partner, and a proud sponsor of this year’s event, Trifacta team is busy meeting with our customers, partners, and data professionals from all corners of the world at the show to discuss how a cloud-native modern data wrangling solution can play a critical role to drive analytics modernization on AWS. Check out my previous blog for what we have going on at the show.
Trifacta’s industry-leading, machine-learning-powered cloud data preparation solution is natively integrated with a rich set of AWS services including Amazon S3, AWS Glue, Amazon IAM, Amazon EMR (for job execution) as well as a wide range of Amazon Machine Learning and AI services within the AWS ecosystem. These native integrations allow customers to take advantage of the elastic scalability, flexibility, security and cost-benefits that AWS has to offer.
Customers across the industry use Trifacta to get data ready on AWS. Those analytics initiatives range from building a cloud data lake on Amazon S3, modernizing BI reporting with Amazon Redshift or Snowflake, to launching ML/AI projects with Amazon Sagemaker and other AI&ML services on AWS. No matter the use case, Trifacta helps streamline the entire data preparation process with an easy-to-use, interactive, machine learning-powered approach. As a result, organizations gain faster time to better analytics insights on AWS.
Data Onboarding with Amazon S3 Data Lake
For customers working on building a data lake on Amazon S3, Trifacta wrangles diverse data in Amazon S3, ensuring clean, well-prepared data lands in the data lake. Trifacta can read and write data to S3, or access data via AWS Glue metadata catalog. Users with different skill sets can visually profile, transform and validate data in an intuitive, machine learning-enabled environment, no need to write code. Customers across the industry such as CropTrak, Origami (subsidiary of Intuit), NationBuilder, Singlewire and many others are refining their S3 data lake with Trifacta. Our customer Malwarebytes uses Trifacta to onboard marketing data from various systems and output high-quality data to their data lake on Amazon S3 to accelerate marketing analytics. By automating the previously time-consuming data wrangling tasks with Trifacta, they are seeing 2X time savings and more accurate results, which in turn delivers better sales outcome.
Modernizing BI Reporting on AWS
For customers who are looking to modernize their BI reporting with Amazon Redshift, Snowflake, Amazon Quicksight, or other advanced reporting tools on AWS, Trifacta allows users to read data from sources such as Amazon S3, Redshift, and Snowflake, wrangle the data in a business-friendly,
interactive environment, and publish the high-quality data to a cloud data warehouse, cloud database or visualization tools such as Tableau to optimize BI reporting. Customers such as Autodesk, American Family Insurance, Adaptive Analytics are relying on Trifacta to fuel their BI reporting with clean, connected and trusted data. At this year’s re:Invent, our customer Autodesk shared their experience of leveraging Trifacta to optimize customer’s financial reporting for better decision-making. Autodesk replaced manual data wrangling with Trifacta and reduced data transformation effort from 3+ hours down to less than an hour in Snowflake on AWS. Trifacta also provides agility to the reporting process in that any update can now be incorporated in the final deliverable in less than a minute versus 3 hours previously.
The result is improved stakeholder interaction, and deeper and more proactive insights for the business.
Accelerating AI & ML With Clean and Relevant Data
Customers working on machine learning and AI projects on AWS can also leverage Trifacta to get data ready for model training. With Trifacta, data scientists and other data workers can visually profile the dataset in Amazon S3, automatically apply a number of transformations to the dataset by leveraging ML-guided suggestions such as Filtering, RapidTarget, or ML-specific transformations such as Binning, Skewness, One-Hot-Encoding to support specific ML use cases. We have helped organizations such as PG&E, Consensus Corp (a Target subsidiary), Vueling to expedite their AI & ML projects by quickly delivering well-prepared data for model training and deployment. For example, Consensus Corp, one of our customers uses Trifacta to wrangle data to fuel their ML-powered fraud detection algorithm on AWS. Leveraging Trifacta’s transformation suggestions for data cleaning as well as automated data pipelines, Consensus reduced the time to model deployment from 3-4 weeks to under 8 hours. In addition, better quality data led to 24% improvement in fraud detection, 55% decrease in false positives, 19% gain in revenue.
Are you in the midst of migrating your data and analytics to AWS? How are you planning on wrangling the data – the most time-consuming part of an analytics workflow, during this major transition? We’d love to hear your stories!
If we missed each other at the show, I encourage you to sign up for Trifacta and get a taste of a modern data wrangling solution for yourself.