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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.
What is Bigeye?
Bigeye is a powerful data observability tool that enables teams to evaluate, improve, and clearly communicate the quality of data at every level. When a data quality issue results in an outage, it can severely undermine an organization’s faith in its data reliability. By implementing proactive monitoring, Bigeye helps restore that confidence by pinpointing missing or erroneous reporting data before it escalates to the executive level. It also sends alerts about potential issues in training data prior to the retraining of models, thus reducing the pervasive uncertainty that often stems from the assumption that most data is typically accurate. It's crucial to understand that the statuses of pipeline jobs may not provide a comprehensive view of data quality; hence, ongoing monitoring of the actual data is vital for confirming its readiness for use. Organizations can monitor the freshness of their datasets to ensure that pipelines function correctly, even during ETL orchestrator disruptions. Moreover, users can observe changes in event names, region codes, product categories, and other categorical data, while also tracking variations in row counts, null entries, and empty fields to ensure that data is being correctly populated. This meticulous approach allows Bigeye to uphold high data integrity standards, which are essential for delivering trustworthy insights that inform strategic decision-making. Ultimately, the comprehensive visibility provided by Bigeye transforms how organizations engage with their data, fostering a culture of accountability and precision.
Integrations Supported
Amazon Redshift
Amazon Web Services (AWS)
Databricks Data Intelligence Platform
Google Cloud BigQuery
MySQL
PostgreSQL
Snowflake
Amazon Athena
Amazon EMR
Azkaban
Integrations Supported
Amazon Redshift
Amazon Web Services (AWS)
Databricks Data Intelligence Platform
Google Cloud BigQuery
MySQL
PostgreSQL
Snowflake
Amazon Athena
Amazon EMR
Azkaban
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
IBM
Date Founded
1911
Company Location
United States
Company Website
www.ibm.com/products/databand
Company Facts
Organization Name
Bigeye
Company Location
United States
Company Website
www.bigeye.com
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