September 2023 Summaries

Poor data quality poses significant threats to businesses, leading to distorted analytical insights and detrimental decision-making. A recent survey conducted by Acceldata reveals that 95% of data leaders spend more than 25% of their time addressing data quality issues, highlighting the pervasive nature of this problem in modern data environments. The consequences of neglecting data quality can be severe, including productivity loss, financial repercussions, and reputational damage. To address these challenges, organizations need to prioritize data reliability as a strategic imperative by investing in tools, processes, and governance that ensure data accuracy, timeliness, and trustworthiness. The Acceldata Data Observability platform is helping leading brands improve their data environments' reliability, productivity, and cost-effectiveness.

The Acceldata platform has surpassed the 0.5 exabyte mark in monthly data observations, demonstrating its capability to handle massive scale data for enterprise deployment. This achievement signifies the importance of scale for enterprise data as companies increasingly adopt a "shift left" approach to data reliability. A platform capable of handling data at massive scale is crucial for early anomaly detection and alerts, comprehensive data coverage, realistic testing, timely resolution, future-proofing, and managing data costs. As data volumes continue to grow rapidly with the rise of IoT, big data analytics, and other data-intensive technologies, maintaining the integrity, accuracy, and reliability of data is critical for enterprises' growth.

The increasing demand for real-time business insights is driving challenges in managing data growth and complexity, as well as talent shortages. Databricks has become a critical tool for enterprise data initiatives, leading to the need for operational control and comprehensive visibility into the platform. To address these issues, Databricks has partnered with Acceldata to provide solutions like Lakehouse Observability and Lakehouse Monitoring. These tools help data teams identify and remediate issues at scale while providing insights across all layers of their data environment. The partnership between Databricks and Acceldata aims to improve the operations of lakehouses and data environments, enabling CDOs, operations, and data teams to better manage data growth, variety, and talent shortages.

Generative AI is significantly impacting the work of data engineers, with its potential to create synthetic data or augment existing data offering additional resources for analysis and management. This increased volume of data can provide more comprehensive analyses and testing without relying solely on actual data, improving the robustness and utility of data-driven models and systems. However, it also presents challenges in terms of data observability, as the newly created data introduces new complexities and nuances that need to be thoroughly understood and managed. AI can enhance data engineering by improving data discovery and access, facilitating data integration and interoperability, automating data analytics tools, and contributing to data democratization. Data observability ensures the reliability, quality, and accuracy of generated data, allowing for real-time monitoring and analysis of this data.

Hadoop is a popular open-source computing framework for processing large datasets, created in 2005 by the Apache Foundation. It is widely used in industry for big data analytics and other data-intensive applications due to its ability to scale horizontally and handle large amounts of data. However, it also presents challenges such as complexity, cost, steep learning curve, and potential security risks. To minimize these risks, data engineering teams should keep their Hadoop clusters up-to-date, secure access to the cluster, use encryption for sensitive data, implement role-based access control, and monitor the cluster regularly. Migration away from Hadoop is another option, with alternatives such as rebuilding on-premises Hadoop clusters in the public cloud or migrating to a modern, cloud-native data warehouse. The Acceldata Data Observability platform can help manage Hadoop environments and ensure a successful migration by providing powerful performance management features and integrating with various environments.

Willem Koenders' analogy likens data management and governance to the components of a tangible structure, encompassing its assets and operations. Enterprise data observability (EDO) functions as a supervisory layer that elevates every facet of data management. EDO can be likened to a cutting-edge building management system for your data assets. By aligning Koenders' perspective with the point of view of EDO, it is explained how data observability enhances and bolsters each facet of data management and governance. Key points include: ensuring data asset reliability, managing data ownership, optimizing data usage and quality, maintaining data security, monitoring data architecture, preserving data domain integrity, enforcing regulatory compliance, managing metadata, assessing data quality, facilitating data remediation, tracking data usage, enabling seamless data integration, and optimizing data storage.

Acceldata has released version 2.9.0 of its Data Observability Cloud (ADOC), offering enhancements in compute, data reliability, monitoring, alerting, and Azure integrations. The update includes new features such as a data freshness policy, dynamic filters for bulk policies, Google Cloud Secret Manager integration, BigQuery lineage data support, enhanced profiling of data assets, and crawling options for single or multiple assets to data sources. Additionally, the latest version introduces new Databricks monitors, Azure Data Factory integrations, actual cost retrieval for Databricks Workspace on Azure, Query Studio enhancements, Snowflake warehouse utilization monitoring, external stage creation for storage integration, and service principal usage for Databricks connection.