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Average Ratings 0 Ratings

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features
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support

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Write a Review

Description

Delta Lake serves as an open-source storage layer that integrates ACID transactions into Apache Spark™ and big data operations. In typical data lakes, multiple pipelines operate simultaneously to read and write data, which often forces data engineers to engage in a complex and time-consuming effort to maintain data integrity because transactional capabilities are absent. By incorporating ACID transactions, Delta Lake enhances data lakes and ensures a high level of consistency with its serializability feature, the most robust isolation level available. For further insights, refer to Diving into Delta Lake: Unpacking the Transaction Log. In the realm of big data, even metadata can reach substantial sizes, and Delta Lake manages metadata with the same significance as the actual data, utilizing Spark's distributed processing strengths for efficient handling. Consequently, Delta Lake is capable of managing massive tables that can scale to petabytes, containing billions of partitions and files without difficulty. Additionally, Delta Lake offers data snapshots, which allow developers to retrieve and revert to previous data versions, facilitating audits, rollbacks, or the replication of experiments while ensuring data reliability and consistency across the board.

Description

Today, there is a considerable amount of discussion surrounding how top-tier companies are leveraging big data to achieve a competitive edge. Your organization aims to join the ranks of these industry leaders. Nevertheless, the truth is that more than 80% of big data initiatives fail to reach production due to the intricate and resource-heavy nature of implementation, often extending over months or even years. The technology involved is multifaceted, and finding individuals with the requisite skills can be prohibitively expensive or nearly impossible. Moreover, automating the entire data workflow from its source to its end use is essential for success. This includes automating the transition of data and workloads from outdated Data Warehouse systems to modern big data platforms, as well as managing and orchestrating intricate data pipelines in a live environment. In contrast, alternative methods like piecing together various point solutions or engaging in custom development tend to be costly, lack flexibility, consume excessive time, and necessitate specialized expertise to build and sustain. Ultimately, adopting a more streamlined approach to big data management can not only reduce costs but also enhance operational efficiency.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AWS Marketplace No 
Actian Data Observability Yes 
Amundsen Yes 
Booz Allen MDR Yes 
Comcast Business VoiceEdge Yes 
DataHub Yes 
Edmunds Financial Management Yes 
IBM StreamSets Yes 
IBM watsonx.data integration Yes 
Informatica Cloud Application Integration Yes 
McGraw-Hill Connect Yes 
MySQL Yes 
Okera Yes 
PuppyGraph Yes 
Subsalt Yes 
Tableau Yes 
Tecton Yes 
TencentDB Yes 
eBay Yes 

Integrations

AWS Marketplace Yes 
Actian Data Observability No 
Amundsen No 
Booz Allen MDR No 
Comcast Business VoiceEdge No 
DataHub No 
Edmunds Financial Management No 
IBM StreamSets No 
IBM watsonx.data integration No 
Informatica Cloud Application Integration No 
McGraw-Hill Connect No 
MySQL No 
Okera No 
PuppyGraph No 
Subsalt No 
Tableau No 
Tecton No 
TencentDB No 
eBay No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Delta Lake

Founded

2019

Country

United States

Website

delta.io

Vendor Details

Company Name

Infoworks

Founded

2014

Country

United States

Website

www.infoworks.io

Product Features

Big Data

Collaboration No 
Data Blends No 
Data Cleansing No 
Data Mining No 
Data Visualization No 
Data Warehousing No 
High Volume Processing No 
No-Code Sandbox No 
Predictive Analytics No 
Templates No 

Product Features

Big Data

Collaboration No 
Data Blends No 
Data Cleansing No 
Data Mining No 
Data Visualization No 
Data Warehousing No 
High Volume Processing No 
No-Code Sandbox No 
Predictive Analytics No 
Templates No 

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