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Bigeye Dependency Driven Monitoring Released

Bigeye announced the availability of Bigeye Dependency Driven Monitoring.

With this launch, Bigeye allows enterprise data teams to connect their analytics dashboards, map every dependency across modern and legacy data sources, and deploy targeted data observability to ensure they stay reliable by default.

Kyle Kirwan, CEO and Co-founder of Bigeye, said, "We've spoken with hundreds of enterprise data leaders and, despite investing heavily in data quality tools and processes, they still struggle to deliver reliable data analytics to business users. Something the data observability industry hasn't yet solved is how to handle the complexity and size of large enterprise data pipelines. This is because enterprise dashboards have a long list of dependencies that span modern and legacy technologies and data observability platforms have yet to offer true support for the types of hybrid environments nearly all Fortune 500 companies have."

Bigeye Dependency Driven Monitoring uniquely solves this challenge by combining enterprise-grade lineage technology with data observability to automatically trace the entire enterprise data pipeline at column-level precision through traditional and modern technologies, ETL stages, and even across the boundary from cloud to on-premises environments.

Bigeye Dependency Driven Monitoring allows data analysts and business users to start from their critical dashboard and—in just a few clicks—enables data observability on each column that matters, and none that don't.

Bigeye Dependency Driven Monitoring provides:

- Faster time to value and improved trust for data consumers.

- Clear visibility into the health of the entire analytics data pipeline for analysts.

- Reduced alert noise and faster issue resolution for data engineers.

- Lower total cost of ownership and less compute overhead for data leaders.

When a data issue is detected, Bigeye will instantly notify each data source owner impacted through Slack or Microsoft Teams. Bigeye can also automatically create a bi-directional ticket in ITSM tools like JIRA and ServiceNow for integrated incident management.

For data consumers, Bigeye will display data health updates directly in their analytics dashboard, providing instant insight into whether or not their analytics are reliable. Data engineering teams can then use Bigeye's lineage-powered root cause and impact analysis to quickly trace the data problem to the source for fast triage and resolution.

Bigeye Dependency Driven Monitoring is powered by Bigeye Lineage Plus, a complete data lineage technology built to handle the largest, most complex enterprise pipelines. Bigeye Lineage Plus includes 50 connectors for transactional databases, cloud data warehouses, data lakes, ETL platforms, analytics tools, and more. Each connector includes parsers that trace lineage at the column level even as it moves from cloud to on-premises sources. As a result, data analysts and data engineers can view a single lineage map of their entire pipeline all the way through to an analytics dashboard or data product.

Bigeye Lineage Plus includes:

- 50+ connectors covering modern and legacy enterprise data sources

- Support for cloud and on-premises infrastructure

- ETL job information capture so no step in the pipeline is lost

Bigeye Lineage Plus connectors for many of the most popular data sources are available today, including Tableau, Microsoft Power BI, Snowflake, Databricks, Google BigQuery, Amazon Redshift, Azure Synapse, IBM DB2, Oracle Database, MySQL, PostgreSQL, Microsoft SQL Server, SAP HANA, and Vertica.

A wide range of additional connectors will be made available throughout 2024, including Informatica PowerCenter, IBM Netezza, Teradata, SAS, Talend, SnapLogic, Apache Spark, IBM DataStage, MicroStrategy, QlikView, SAP Business Objects, Tibco Spotfire, and others.

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Bigeye Dependency Driven Monitoring Released

Bigeye announced the availability of Bigeye Dependency Driven Monitoring.

With this launch, Bigeye allows enterprise data teams to connect their analytics dashboards, map every dependency across modern and legacy data sources, and deploy targeted data observability to ensure they stay reliable by default.

Kyle Kirwan, CEO and Co-founder of Bigeye, said, "We've spoken with hundreds of enterprise data leaders and, despite investing heavily in data quality tools and processes, they still struggle to deliver reliable data analytics to business users. Something the data observability industry hasn't yet solved is how to handle the complexity and size of large enterprise data pipelines. This is because enterprise dashboards have a long list of dependencies that span modern and legacy technologies and data observability platforms have yet to offer true support for the types of hybrid environments nearly all Fortune 500 companies have."

Bigeye Dependency Driven Monitoring uniquely solves this challenge by combining enterprise-grade lineage technology with data observability to automatically trace the entire enterprise data pipeline at column-level precision through traditional and modern technologies, ETL stages, and even across the boundary from cloud to on-premises environments.

Bigeye Dependency Driven Monitoring allows data analysts and business users to start from their critical dashboard and—in just a few clicks—enables data observability on each column that matters, and none that don't.

Bigeye Dependency Driven Monitoring provides:

- Faster time to value and improved trust for data consumers.

- Clear visibility into the health of the entire analytics data pipeline for analysts.

- Reduced alert noise and faster issue resolution for data engineers.

- Lower total cost of ownership and less compute overhead for data leaders.

When a data issue is detected, Bigeye will instantly notify each data source owner impacted through Slack or Microsoft Teams. Bigeye can also automatically create a bi-directional ticket in ITSM tools like JIRA and ServiceNow for integrated incident management.

For data consumers, Bigeye will display data health updates directly in their analytics dashboard, providing instant insight into whether or not their analytics are reliable. Data engineering teams can then use Bigeye's lineage-powered root cause and impact analysis to quickly trace the data problem to the source for fast triage and resolution.

Bigeye Dependency Driven Monitoring is powered by Bigeye Lineage Plus, a complete data lineage technology built to handle the largest, most complex enterprise pipelines. Bigeye Lineage Plus includes 50 connectors for transactional databases, cloud data warehouses, data lakes, ETL platforms, analytics tools, and more. Each connector includes parsers that trace lineage at the column level even as it moves from cloud to on-premises sources. As a result, data analysts and data engineers can view a single lineage map of their entire pipeline all the way through to an analytics dashboard or data product.

Bigeye Lineage Plus includes:

- 50+ connectors covering modern and legacy enterprise data sources

- Support for cloud and on-premises infrastructure

- ETL job information capture so no step in the pipeline is lost

Bigeye Lineage Plus connectors for many of the most popular data sources are available today, including Tableau, Microsoft Power BI, Snowflake, Databricks, Google BigQuery, Amazon Redshift, Azure Synapse, IBM DB2, Oracle Database, MySQL, PostgreSQL, Microsoft SQL Server, SAP HANA, and Vertica.

A wide range of additional connectors will be made available throughout 2024, including Informatica PowerCenter, IBM Netezza, Teradata, SAS, Talend, SnapLogic, Apache Spark, IBM DataStage, MicroStrategy, QlikView, SAP Business Objects, Tibco Spotfire, and others.

The Latest

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...

In March, New Relic published the State of Observability for Media and Entertainment Report to share insights, data, and analysis into the adoption and business value of observability across the media and entertainment industry. Here are six key takeaways from the report ...

Regardless of their scale, business decisions often take time, effort, and a lot of back-and-forth discussion to reach any sort of actionable conclusion ... Any means of streamlining this process and getting from complex problems to optimal solutions more efficiently and reliably is key. How can organizations optimize their decision-making to save time and reduce excess effort from those involved? ...

As enterprises accelerate their cloud adoption strategies, CIOs are routinely exceeding their cloud budgets — a concern that's about to face additional pressure from an unexpected direction: uncertainty over semiconductor tariffs. The CIO Cloud Trends Survey & Report from Azul reveals the extent continued cloud investment despite cost overruns, and how organizations are attempting to bring spending under control ...

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Azul