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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.

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In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

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 ...