January 2022 Summaries

Acceldata has been awarded the 2021 North American Data Observability Technology Innovation Leadership Award by Frost & Sullivan for its innovative excellence in the field. The company's data observability platform helps enterprises achieve operational excellence, innovation agility, and higher returns on their data initiatives. Acceldata focuses on providing comprehensive visibility into data environments and empowering data engineers with efficient tools to manage data systems effectively. Frost & Sullivan's research process includes evaluating vendors based on criteria such as commitment to innovation, customer acquisition success, and human capital growth potential. The report highlights Acceldata's ability to deliver end-to-end visibility into all data sets, systems, and processes, enabling a holistic approach for data engineers and operations teams.

The text discusses the importance of reliable data in machine learning and how it is currently a bottleneck for many companies. Despite advancements in AI, the lack of trustworthy data pipelines limits the widespread application of ML. Data observability platforms like Acceldata are seen as potential solutions to this problem by providing visibility into distributed data pipelines and improving their reliability. The author believes that startups addressing the data engineering bottleneck will have a significant impact on modern data environments, and has joined Acceldata due to its technical expertise and market traction.

Data observability is crucial in modern data environments due to increasing data volumes and operational demands. It involves understanding the health of an organization's data by monitoring and correlating data workload events across application, data, and infrastructure layers. A multidimensional data observability platform like Acceldata provides visibility into data, infrastructure, and pipelines, helping data engineers ensure optimal operations of their data infrastructure investments. Key features to look for in a data observability platform include data discovery, data quality rules, data drift detection, compute performance monitoring, pipeline monitoring, and ETL integration. Implementing such a platform can help organizations monitor, detect, predict, prevent, and resolve issues across their data, processing, and pipelines.

A "data swamp" refers to an inefficiently managed data environment that leads to increased costs, delays, and missed opportunities. It often emerges from a lack of visibility and control due to factors such as data democratization pitfalls, cloud computing challenges, M&A activities, external data risks, turnover, real-time use cases, advanced analytics, and trust issues. Acceldata's Data Observability Platform can help address these problems by providing a comprehensive 360-degree view of the data environment, enabling users to identify redundant or unused data, improve data management and discovery, optimize storage for cost savings, automate classification and tagging, monitor utilization and redundancy, understand interdependencies, build trust in data, and ultimately accelerate digital transformation, increase data ROI, and achieve better outcomes.

Acceldata Pulse On-Prem 1.8.1, a compute performance monitoring platform, has been released to enhance data processing reliability, scale, and cost. The platform offers real-time intelligence for observability needs, algorithmic anomaly detection, recommendations, and automation to maintain data systems' performance, security, and reliability. Key features include job monitoring, efficient debugging, advanced root cause analysis, customized recommendations, native integration with data engines, and a powerful Javascript-based dashboard builder. The On-Prem version 1.8.1 addresses issues such as obsolete audit logs, unresponsive options in Mongo data source visualizations, and upgrades Apache Log4j 2 to version 2.17.0 for vulnerability fixes.

Gartner predicts that only 20% of data and analytics will result in real business outcomes, highlighting the need for enterprises to address deeply entrenched data problems such as silos, inaccessible data/analytics, and over-reliance on manual interventions. Acceldata offers a multidimensional data observability solution that can help data teams avoid these issues and achieve better business outcomes. Data engineering teams can use this approach to address three significant data problems: data silos within the enterprise, poor quality plus inaccessible data and analytics, and relying only on manual data interventions. By leveraging AI and automation, Acceldata's suite of data observability solutions helps even small data teams improve their analytic capabilities and achieve better business outcomes from their data initiatives.

Acceldata Torch is a multidimensional data quality solution for data lakes and warehouses that automates data quality and reliability across the entire pipeline. It monitors enterprise data, scales workloads, validates business rules, manages taxonomy of sensitive data assets, reconciles data during cloud migrations, detects schema drift, automates anomaly detection, and accelerates data discovery, exploration, and validation. The latest version, Torch 1.2.0, comes with various enhancements to improve its functionality.