Tuesday, April 13, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Big Data

Trifacta goes all in on the cloud

April 6, 2021
in Big Data
Trifacta goes all in on the cloud
585
SHARES
3.2k
VIEWS
Share on FacebookShare on Twitter

Trifacta, which has become the last pure play data prep tools provider still standing, sees its future as a broader based cloud software-as-a-service (SaaS) service. This week, it is unveiling a new Data Engineering Cloud that will deliver a fully managed service on each of the major clouds. That will be in addition to, not instead of Wrangler, its long-established on-premises prep suite.

Trifacta’s niche will continue to be serving as the front end design studio where the data engineer, data scientist, or business developer creates the “recipes” for data preparation and transformation. The Trifacta Data Engineering Cloud will extend beyond data prep to encompass cleansing, validation, profiling, and the monitoring of data pipelines. But those pipelines will run in the downstream execution tool of choice. The Trifacta Data Engineering Cloud service won’t replace the Databricks or Snowflakes of the world, but instead let users run data prep inside them. And, as for Databricks, Trifacta is also announcing today that it is taking the partnership up a notch with native integration of its data prep pipelines into the Lakehouse platform that is built around Delta Lake.

You might also like

Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL

MinIO simplifies onramps to do-it-yourself hybrid cloud object storage

Cloudera Data Platform hits Google Cloud

In the run-up to the announcement, Trifacta has had a good dress rehearsal for the SaaS service as the OEM partner behind Google Cloud Dataprep. The GCP offering put the Trifacta suite on a cloud-native platform running on Kubernetes (K8s), and while it was initially focused on ELT working with Google BigQuery and cloud storage, it recently added a premium tier that added support for non-Google data sources such as Oracle, SQL Server, MySQL, PostgreSQL, and salesforce.com. The premium edition serves as a prelude to the new Trifacta Data Engineering Cloud offering, which also takes advantage of the microservices and K8s architecture of the Google offering to provide the cookie cutter template for rollout to other clouds.

Beyond multi-cloud support, the Trifacta offering broadens beyond the no-code, drag and drop tool for business analyst to provide multiple pathways for designing data preparation. It now offers three views. It includes the original “grid” view, that provided the spreadsheet view for data preparation tasks, where values were reconciled to the right columns. Then it adds a flow view, which shows the entity relationships familiar to SQL developers, and the “code” view that is suited for Python programmers. While SQL developers can use  DBT (Data Building tool) for writing transformations using SQL Select statements, data scientists can write transforms in Python from their Jupyter notebooks; the results populate Trifacta recipes that are handed down to execution environments. A rich library of 180+ connectors are also provided. Once the recipes are created, they can be integrated into the data pipelines or workflows of external tools or services, such as Databricks, through APIs.

When Trifacta emerged roughly a decade ago, data preparation was targeted at data lakes, viewed as a rough-cut alternative to traditional ETL tools, typically using a spreadsheet-like interface where rudimentary machine learning capabilities would suggest columns names, spot specific types of data patterns such as street address, names, or personally-identifiable data such as account numbers, and then suggest which columns could be consolidated and modest corrections to make data more correct or uniform.

These capabilities eventually became commodity, and as such, ended up getting incorporated into ETL suites, data science tools, data catalogs, and so on. Unlike the old days of enterprise data warehousing, where IT or database developers handled data transformation, data preparation became a broad-based responsibility as end users, from business analysts to data scientists, clamored for self-service. Instead of forcing these folks into different tools, data prep grew ubiquitous in their existing workspaces and tools of choice.

Also: What is low-code and no-code? A guide to development platforms

Not surprisingly, most of Trifacta’s pure play rivals have either disappeared or been acquired, among them, Paxata by Data Robot less than a year and a half ago. At this point, Alteryx, which also positions itself as an “analytics process automation” workbench for citizen data scientists, remains Trifacta’s best-known rival. 

Not surprisingly, with core data prep functions commoditized, the new Trifacta offering goes beyond that with predictive transformation that autodetects data formats and structures and infers transformation logic; “adaptive” data quality that statistically profiles data to identify complex patterns and suggest transformation rules; and “smart” data pipelines that model data flows. While data integration, data science, and analytic tools cover data prep, Trifacta is positioning its Data Engineering Cloud as a more deluxe service.

With the new cloud service, not surprisingly, Trifacta is rolling out consumption-based pricing, providing a contrast to the traditional licensing of its Wrangler on-premises suite. It’s an expected route for SaaS providers, and for Trifacta, is intended to open up its addressable market beyond large enterprises that start with six-figure investments with tiers that start with free trials and starter subscriptions at $80/month.

The service, not surprisingly, is patterned off and expands on the OEM service that Trifacta has delivered with Google for the past three years. There will be feature parity across AWS and Azure, in addition to GCP. Nonetheless, GCP will remain first among equals as a jointly supported and sold OEM offering natively integrated to BigQuery.

Trifacta’s challenge is akin to that of third party databases or analytic tools that are not the captive of a specific cloud provider, analytics tool, or data science workspace. It’s the classic choice between umbrella platform vs. best of breed, and single cloud vs. multi-cloud. For Trifacta, it is enterprises whose data assets and analytic platforms are heterogenous and likely to remain so. With APIs, Trifacta aims to embed its data engineering services into the workflows of whatever runtimes that business analysts, data engineers, or data scientists are using. Thanks to its three years running an OEM service on Google Cloud, Trifacta is not entering the world of SaaS as a rookie.

Credit: Zdnet

Previous Post

Aporia raises $5M for its AI observability platform – TechCrunch

Next Post

MES: The path to intelligent manufacturing

Related Posts

Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL
Big Data

Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL

April 8, 2021
MinIO simplifies onramps to do-it-yourself hybrid cloud object storage
Big Data

MinIO simplifies onramps to do-it-yourself hybrid cloud object storage

April 7, 2021
Cloudera Data Platform hits Google Cloud
Big Data

Cloudera Data Platform hits Google Cloud

March 31, 2021
Cloudera fills gap in streaming platform with SQL
Big Data

Cloudera fills gap in streaming platform with SQL

March 31, 2021
Databases, graphs, and GraphQL: The past, present, and future
Big Data

Databases, graphs, and GraphQL: The past, present, and future

March 30, 2021
Next Post
MES: The path to intelligent manufacturing

MES: The path to intelligent manufacturing

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

A.I. For Raspberry Pi Pico: Uctronics TinyML Learning Kit Review
Machine Learning

A.I. For Raspberry Pi Pico: Uctronics TinyML Learning Kit Review

April 13, 2021
BERT Transformers — How Do They Work? | by James Montantes | Apr, 2021
Neural Networks

BERT Transformers — How Do They Work? | by James Montantes | Apr, 2021

April 13, 2021
Bug bounties: More hackers are spotting vulnerabilities across web, mobile and IoT
Internet Security

Critical security alert: If you haven’t patched this old VPN vulnerability, assume your network is compromised

April 13, 2021
Epoch and Map of the Energy Transition through the Consensus Validator
Data Science

Epoch and Map of the Energy Transition through the Consensus Validator

April 13, 2021
Bitcoin mining in China could threaten climate policies, new study shows
Blockchain

Bitcoin mining in China could threaten climate policies, new study shows

April 13, 2021
Artificial Intelligence Research at Duke
Machine Learning

Artificial Intelligence Research at Duke

April 13, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • A.I. For Raspberry Pi Pico: Uctronics TinyML Learning Kit Review April 13, 2021
  • BERT Transformers — How Do They Work? | by James Montantes | Apr, 2021 April 13, 2021
  • Critical security alert: If you haven’t patched this old VPN vulnerability, assume your network is compromised April 13, 2021
  • Epoch and Map of the Energy Transition through the Consensus Validator April 13, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates