As the insatiable demand for data grows, enterprise data teams continue to face organizational, management, and utilization challenges to how they manage their data growth and data environment complexity. The increasingly real-time nature of business insights demands an approach to data management that has a material effect on how these data teams actually deliver value. For today’s data leaders, that involves reducing complexity, optimizing costs, and ensuring data reliability.
In addition to data growth and complexity, Chief Data Officers (CDO) face talent shortages and must deal with the reality of managing more data with fewer specialists. While this is happening, they are also being tasked with maintaining a continuously reliable data foundation to meet the demands of the business. Responsibility for execution is often distributed between CDOs and Operations execs and their respective teams. Bridging the gap between them requires a framework for defining and implementing data initiatives without creating even more data engineering projects that stress already resource-constrained teams.
As Databricks has become mission-critical to enterprise data initiatives, the need for real-time operational control and comprehensive visibility into the platform has become a top priority. Databricks is taking a leadership role in enabling CDOs and their data teams to ensure high value, reliable data ROI.
Databricks Lakehouse Observability and Lakehouse Monitoring is a Game Changer for Data Observability
Databricks seamlessly incorporates lakehouse observability for effective data, analytics, ML, and AI efforts through Unity Catalog features such as Lakehouse Observability and Lakehouse Monitoring.
A large variety of operational metrics captured by Lakehouse Observability are connected through a consistent and comprehensive set of systems tables within Databricks. Using any Lakehouse language, including SQL, Python, Scala, and R, this unified platform enables Lakehouse administrators and developers to generate crucial data, facilitating a deeper knowledge of operational patterns and exposing potential problems. System tables serve a dual purpose: they are intended for both direct use and as a base for working with Databricks partners like Acceldata, who can create complementary solutions that deliver operational data of a much higher caliber than manual methods like extracting data from APIs and ingesting log files.
In addition, Lakehouse Monitoring focuses on data quality, data profiling, carrying out fundamental quality checks, and tracking data drift. Users who utilize the Unity Catalog fast access to in-depth data profiling and high-quality insights thanks to the useful information that is logically grouped there.
A Partnership That Extends and Automates Lakehouse Observability
Enterprise data teams need the ability to identify and remediate issues at scale as early as possible in the data journey, while providing insights across all layers of the data stack. Acceldata operationalizes new Lakehouse Observability and Monitoring capabilities, leveraging metadata stored in Databricks’ new system tables. This gives data teams a comprehensive, single source for monitoring, alerting, remediating, and making recommendations for the health of their data across all aspects of their data environment. The Acceldata Data Observability Platform has been developed and continuously improved through insights derived from best practices from the most complex deployments at some of the largest corporations in the world.
Previously, data teams had few choices about how they solved data complexity. The logical direction was to build home grown solutions for operational insights and data reliability, which required the valuable time and resources of data engineering teams. That’s still happening today, yet data teams recognize that that time could be better spent focusing on activating revenue generating business use-cases. As a result, they are turning to Acceldata to provide the platform for data reliability across their complex data environments.
“With Acceldata, we’ve been able to shift-left our data reliability, which means we’re remediating data issues before they become a problem. That’s saving our customers money and they love us for it.” - VP of Data Management @ global information provider
Gartner points out that data observability must provide a 360o view across the data content, data pipelines, infrastructure performance, cost and utilization of data platforms while enabling data owners to proactively identify and resolve issues. Only then can an enterprise CDO deliver trust across their multi-cloud, multi-technology environments.
Acceldata powers a comprehensive, multi-dimensional data operations experience through a deep integration with Databricks’ Data Observability initiatives for Databricks customers. With Acceldata’s expertise in data reliability, operational management, and cost to value alignment, Databricks customers get the benefits of a consistent data operations structure that enterprise data teams can use in their Databricks environments and across the entire data ecosystem. Now, rather than manually attending to the vast array of data activity in their environments, these teams can focus on creating data ROI.
Data Reliability is accomplished by continuously analyzing lakehouse assets, data workflow patterns and observability of data pipeline behavior. It identifies deviations from expected norms, such as schema drifts, data drifts, sudden spikes in data volume or processing delays, alerting users to potential bottlenecks, data quality issues, and other risks. This early anomaly detection aids in preventing issues, and enables swift remediation before these issues impact data integrity or system performance.
Acceldata data reliability provides insights into data-at-rest, data-in-motion, and data-for-consumption for large volumes of data headed into Databricks Lakehouses or out of it for consumption. Data reliability rules and policies can be defined in a global library and applied on the semantic definitions of data attributes. This allows for consistent reapplication of reliability rules at scale across any number of data sources and business domains as the semantics of the underlying data are identified during data onboarding and profiling. A robust data reliability reporting framework allows for platform and domain level reporting and auditability for key data stakeholders.
Operational Intelligence delivers comprehensive insights on data infrastructure delivered by Databricks. It provides a view of performance and resource utilization at a multi-cluster perspective, with dashboards that drill down into details such as individual job executions, event correlation, and historical comparison. Recommendations can be derived on how to allocate resources that ensure platform efficiency and optimal performance, as well as applying guardrails that prevent runaway resource use or excess consumption by processes or users.
Spend Intelligence facilitates a thorough and accurate understanding of Databricks consumption and expenditures while enabling informed decision-making for identifying user-level resource waste. Cost explorating, attribution and trends dashboards track and analyze spend and utilization by resource, cluster, business owner, users/account, and job executions. Built-in department or project-level chargeback and budgeting help to analyze contract plans, forecast spending, and optimize cost allocation to efficiently allocate funds to internal business entities. Utilizing advanced query parsing and fingerprinting techniques aids in the detection of inconsistent queries, facilitating comprehension and optimization for improved performance and cost-effectiveness in workloads.
A Partnership That Continues to Build and Innovate
The Databricks Lakehouse Observability and Monitoring framework provides partners with direct access to the information they need to incorporate Databricks metrics into the charter of data observability. Acceldata is continually building and enriching on the underlying framework with capabilities such as:
- Cluster efficiency analysis, sizing recommendations, and monitoring with automated termination
- Workload performance, cost and user analysis - powered by query fingerprinting
- Scalable, operational application for each dimension of data reliability, which includes data quality, data reconciliation, schema drift, data drift and data anomalies, and data freshness/volume SLAs and anomalies
- Visibility across all of an organization's data pipelines for monitoring and alerting
The operationalization of Databricks Lakehouse Observability and Monitoring is just the beginning of an ongoing partnership that illustrates how CDOs, operations, and data teams benefit when they establish data observability as the foundation for managing data growth, data variety, and addressing talent shortages that plague every enterprise. As Databricks expands its platform with trusted partners' innovations, Acceldata remains devoted to this collaboration for the shared benefit of our customers.
See for yourself how data observability can improve the operations of your lakehouses and data environment - sign up for a demo of the Acceldata Data Observability platform.
Photo by Marc-Olivier Jodoin on Unsplash
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