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Description

Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.

Description

Create a comprehensive understanding of quality that empowers timely and assured decision-making. Swiftly identify areas where assistance for quality enhancement is required, ensuring quicker responses. Propel your quality improvement initiatives to achieve superior outcomes in a shorter timeframe, all while maintaining transparency and accountability. Improve fosters a collective approach to planning for enhancement throughout the organization, enabling effective distribution and management of responsibilities related to improvement tasks among team members. You can effortlessly establish deadlines, track advancements, and record both expected and actual outcomes. Observe facilitates collaborative, trustworthy, and developmental assessments of practices in various formats, such as learning walks, peer support, and individual reflections. The platform simplifies the processes of organizing, executing, and evaluating observational activities, allowing you to emphasize sharing effective practices and providing necessary support. With all pertinent information readily available, you can make swift and informed decisions with confidence. Ultimately, this approach not only enhances quality but also nurtures a culture of continuous improvement within the organization.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AWS Glue Yes 
Amazon S3 Yes 
Amazon Web Services (AWS) Yes 
Apache Airflow Yes 
Azure Cosmos DB Yes 
Azure SQL Database Yes 
Cloudera Yes 
Databricks Yes 
Google Cloud BigQuery Yes 
Google Cloud Dataflow Yes 
Google Cloud Platform Yes 
Microsoft Azure Yes 
PostgreSQL Yes 
SQL Server Yes 
Snowflake Yes 
Teradata VantageCloud Yes 

Integrations

AWS Glue No 
Amazon S3 No 
Amazon Web Services (AWS) No 
Apache Airflow No 
Azure Cosmos DB No 
Azure SQL Database No 
Cloudera No 
Databricks No 
Google Cloud BigQuery No 
Google Cloud Dataflow No 
Google Cloud Platform No 
Microsoft Azure No 
PostgreSQL No 
SQL Server No 
Snowflake No 
Teradata VantageCloud No 

Pricing Details

Consumption-based and annual fixed licensing fee are both available.
Free Trial No 
Free Version No 

Pricing Details

$181.21 per month
Free Trial No 
Free Version Yes 

Deployment

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

Customer Support

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

Customer Support

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

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

FirstEigen

Founded

2015

Country

United States

Website

firsteigen.com/databuck/

Vendor Details

Company Name

Mesma

Country

United Kingdom

Website

mesma.co.uk

Product Features

Big Data

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

Data Governance

Access Control No 
Data Discovery No 
Data Mapping No 
Data Profiling No 
Deletion Management No 
Email Management No 
Policy Management No 
Process Management No 
Roles Management No 
Storage Management No 

Data Management

Customer Data No 
Data Analysis No 
Data Capture No 
Data Integration No 
Data Migration No 
Data Quality Control No 
Data Security No 
Information Governance No 
Master Data Management No 
Match & Merge No 

Data Quality

Address Validation No 
Data Deduplication No 
Data Discovery No 
Data Profililng Yes 
Master Data Management No 
Match & Merge No 
Metadata Management No 

Product Features

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