Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Creating and overseeing a thoroughly documented data project requires significant time and extensive manual coding, but that is no longer the case. We are confident in our ability to help you improve data transformation efficiency, and we can back that promise with results. Our column-aware architecture facilitates the reuse of data patterns and efficient change management on a large scale. By enhancing visibility around change management and impact analysis, we ensure safer and more predictable data operations. Coalesce offers specially curated packages containing best-practice templates that can automatically generate native-SQL for Snowflake™. If you have specific requirements, rest assured that our templates are fully customizable to suit your needs. Navigating through your data pipeline is a breeze with Coalesce, as every screen and button has been thoughtfully designed for easy access to all necessary tools. With Coalesce, your data team gains enhanced control over projects, allowing for features like side-by-side code comparison and immediate visibility into project and audit histories. Additionally, we guarantee that table-level and column-level lineage information is continuously updated and readily available, ensuring that your data remains accurate and reliable. Ultimately, Coalesce empowers your team to optimize workflows and focus on delivering insights rather than getting bogged down in administrative tasks.
Description
Examine the usage of your data assets, focusing on aspects like popularity, utilization, and schema coverage. Gain vital insights into your data assets, including their quality and usage metrics. You can easily locate and filter the necessary data by leveraging metadata tags and descriptions. Additionally, these insights will help you drive data governance and establish clear ownership within your organization. By implementing a streamlined lineage from data lakes to warehouses, you can enhance collaboration and accountability. An automatically generated field-level lineage map provides a comprehensive view of your entire data ecosystem. Moreover, anomaly detection systems adapt by learning from your data trends and seasonal variations, ensuring automatic backfilling with historical data. Thresholds driven by machine learning are specifically tailored for each data segment, relying on actual data rather than just metadata to ensure accuracy and relevance. This holistic approach empowers organizations to better manage their data landscape effectively.
API Access
Has API
No
API Access
Has API
No
Integrations
Snowflake
Yes
Amazon Kinesis
No
Amazon Redshift
No
Amazon S3
No
Apache Kafka
No
Azure Data Lake
No
Azure Synapse Analytics
No
Databricks
No
Git
Yes
Gmail
No
Integrations
Snowflake
Yes
Amazon Kinesis
Yes
Amazon Redshift
Yes
Amazon S3
Yes
Apache Kafka
Yes
Azure Data Lake
Yes
Azure Synapse Analytics
Yes
Databricks
Yes
Git
No
Gmail
Yes
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
No
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
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
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
Coalesce.io
Founded
2020
Country
United States
Website
coalesce.io
Vendor Details
Company Name
Validio
Founded
2019
Website
validio.io
Product Features
Data Lineage
Database Change Impact Analysis
No
Filter Lineage Links
No
Implicit Connection Discovery
No
Lineage Object Filtering
No
Object Lineage Tracing
No
Point-in-Time Visibility
No
User/Client/Target Connection Visibility
No
Visual & Text Lineage View
No
ETL
Data Analysis
No
Data Filtering
No
Data Quality Control
No
Job Scheduling
No
Match & Merge
No
Metadata Management
No
Non-Relational Transformations
No
Version Control
No
Product Features
Data Lineage
Database Change Impact Analysis
No
Filter Lineage Links
No
Implicit Connection Discovery
No
Lineage Object Filtering
No
Object Lineage Tracing
No
Point-in-Time Visibility
No
User/Client/Target Connection Visibility
No
Visual & Text Lineage View
No
Data Quality
Address Validation
No
Data Deduplication
No
Data Discovery
No
Data Profililng
No
Master Data Management
No
Match & Merge
No
Metadata Management
No