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What is Informatica Data Quality?

Gain immediate strategic benefits by providing extensive support that meets the changing requirements of data quality for various users and data types through automation powered by AI. Whether your organization is involved in data migration or sophisticated analytics, Informatica Data Quality supplies the necessary adaptability to implement data quality seamlessly in any situation. By empowering business users, you can also improve collaboration between IT departments and business executives. Maintain oversight of data quality across both multi-cloud and on-premises environments for a wide range of applications and workloads. Incorporate human intervention within the workflow, allowing business users to evaluate, modify, and authorize exceptions during the automated process as needed. Execute data profiling and ongoing analysis to uncover relationships and more efficiently pinpoint problems. Utilize AI-generated insights to automate vital tasks and enhance data discovery processes, which in turn increases productivity and operational efficiency. This all-encompassing strategy not only improves data quality but also encourages a culture of ongoing enhancement throughout the organization, ultimately positioning your business for future growth and success.

What is IBM Databand?

Monitor the health of your data and the efficiency of your pipelines diligently. Gain thorough visibility into your data flows by leveraging cloud-native tools like Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. This observability solution is tailored specifically for Data Engineers. As data engineering challenges grow due to heightened expectations from business stakeholders, Databand provides a valuable resource to help you manage these demands effectively. With the surge in the number of pipelines, the complexity of data infrastructure has also risen significantly. Data engineers are now faced with navigating more sophisticated systems than ever while striving for faster deployment cycles. This landscape makes it increasingly challenging to identify the root causes of process failures, delays, and the effects of changes on data quality. As a result, data consumers frequently encounter frustrations stemming from inconsistent outputs, inadequate model performance, and sluggish data delivery. The absence of transparency regarding the provided data and the sources of errors perpetuates a cycle of mistrust. Moreover, pipeline logs, error messages, and data quality indicators are frequently collected and stored in distinct silos, which further complicates troubleshooting efforts. To effectively tackle these challenges, adopting a cohesive observability strategy is crucial for building trust and enhancing the overall performance of data operations, ultimately leading to better outcomes for all stakeholders involved.

Media

Media

Integrations Supported

Amazon EMR
Amazon Redshift
Amazon S3
Amazon Web Services (AWS)
Apache Airflow
Apache Spark
Azkaban
Databricks Data Intelligence Platform
Delta Lake
Google Cloud BigQuery
Google Cloud Composer
Google Cloud Dataproc
Google Cloud Storage
Kubernetes
Microsoft Azure
MySQL
PostgreSQL
Python
Scala
Snowflake

Integrations Supported

Amazon EMR
Amazon Redshift
Amazon S3
Amazon Web Services (AWS)
Apache Airflow
Apache Spark
Azkaban
Databricks Data Intelligence Platform
Delta Lake
Google Cloud BigQuery
Google Cloud Composer
Google Cloud Dataproc
Google Cloud Storage
Kubernetes
Microsoft Azure
MySQL
PostgreSQL
Python
Scala
Snowflake

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Informatica

Date Founded

1993

Company Location

United States

Company Website

www.informatica.com/products/data-quality.html

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/products/databand

Categories and Features

Data Quality

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

Categories and Features

Data Lineage

Database Change Impact Analysis
Filter Lineage Links
Implicit Connection Discovery
Lineage Object Filtering
Object Lineage Tracing
Point-in-Time Visibility
User/Client/Target Connection Visibility
Visual & Text Lineage View

Data Preparation

Collaboration Tools
Data Access
Data Blending
Data Cleansing
Data Governance
Data Mashup
Data Modeling
Data Transformation
Machine Learning
Visual User Interface

Data Quality

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

Data Visualization

Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery

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