June 2023 Summaries

Treasure hunters and enterprise data teams share similarities in their quest for discovery. Both rely on specialized tools to navigate through vast areas, whether it's a physical terrain or an ocean of data. Data observability is crucial for data teams as it helps improve data discovery by eliminating the need for manual location of files, updating catalogs, and maintaining metadata logs. This leads to better return on investment in data and increased value. Data environments are constantly evolving, making continuous evaluation necessary. Data observability tools help pinpoint where changes should be made and advantages capitalized upon to uncover data value. However, discovering the right data can be challenging due to information silos within enterprises. Different teams often create separate datasets based on their specific needs, leading to limited cross-functional insights. Manual code, catalogs, and metadata work but are not scalable as they cannot keep up with the increasing volume, variety, and velocity of incoming data. Data observability improves data discovery at scale by identifying data quality and reliability issues, checking for schema and data drift, reconciling data in motion, and offering self-service catalogs and metadata profiles. Advancements in data observability eliminate time-consuming manual processes of finding the data needed, automatically updating data catalogs and metadata profiles across all data sources, making them rapidly discoverable.

Data observability is crucial for managing diverse data sources in modern data environments. It provides real-time visibility into the health, performance, and quality of data pipelines and sources, enabling proactive monitoring and detection of anomalies, errors, and bottlenecks in data ingestion and processing. By implementing data observability, teams can ensure the reliability, accuracy, and timeliness of data, identify and resolve issues promptly, and maintain the overall integrity of the data environment. Data observability also facilitates performance monitoring, allowing data engineers to track key metrics such as data latency, throughput, and processing times. This enables organizations to optimize their pipelines for better efficiency and responsiveness. Additionally, data observability plays a crucial role in aligning Service Level Agreements (SLAs) with new data sources by providing real-time monitoring and analysis capabilities.

Enterprises are increasingly prioritizing data quality and reliability to develop data products and services. To achieve this at scale, they are employing data observability. For those using Databricks, the Acceldata Data Observability platform provides an optimal structure for ensuring data reliability and a framework for optimizing performance and cost. The integration with Databricks gives users comprehensive operational observability into their Apache Spark deployments. Databricks functions as the de-facto cloud platform for Spark, allowing users to perform cluster management and deploy Spark applications in cloud environments. It is an open-source unified data analytics engine for large scale data processing and supports Spark users with expressive (Scala and Python) and simple (Spark SQL) ways of working with petabyte-scale data. Acceldata helps enterprise data teams observe their cluster and the performance of their jobs, enabling them to implement data reliability techniques for Delta Lake on Databricks. The platform provides visualization for spend tracking, cluster or compute usage, workflow debugging, and data insights. It also offers actionable insights, anomaly detection, alerting, reporting, and guardrail recommendations for robust administration of Databricks accounts.

Data engineers face increasing demand for new data workflows while maintaining high levels of data quality. To scale data reliability efforts, an approach that drives how data operates throughout its lifecycle is required, along with specific guidelines and prescription actions. Acceldata's Data Observability platform provides the ability to apply bulk policies to the guidelines and rules for data sources, ensuring data reliability at scale. The Bulk Policies feature allows users to create a set of rules to be added to the data quality policy and group them together, then apply the rule group to a data source. Acceldata's intuitive charts and graphs offer valuable context to help users understand their data and its sources, activity, and status.

Acceldata has released version 2.7.0 of its Data Observability Cloud (DOC), which introduces significant enhancements to Databricks and Snowflake observability, as well as support for new data sources such as Teradata, Azure MSSQL, and Oracle. The update includes a variety of major enhancements like the introduction of Databricks Query Studio experience, enhanced Databricks Guardrails, advanced cost center management with Tag Pairs, streamlined Databricks navigation, enhanced Snowflake Query Studio, projected spend budget alerts, and more. Additionally, data reliability capabilities have been improved with better data cadence support, importing/exporting policies, service monitoring, policy execution, and others. The update also includes expanded notification channels, a new email notification template for assignee action, and enhanced recommendations.

In the retail industry, data observability plays a crucial role in various use cases such as forecasting and replenishment, inventory optimization, predictive pricing, smart merchandising, assortment planning, recommendation engines, and behavioral analytics. Data observability helps ensure the accuracy and optimal pricing of products by identifying and addressing data issues that may impact pricing models. By monitoring data pipelines and ensuring data quality, retailers can make informed decisions, align their inventory with market trends, and optimize assortment planning. Retailers deal with complex data pipelines involving multiple systems, both within the organization and those from external sources. Data observability ensures that behavioral analytics models are aligned with the most accurate and up-to-date data, enhancing the effectiveness of targeted marketing efforts.