December 2021 Summaries

Managing IT costs and maximizing ROI has become increasingly difficult due to the complexity of modern data infrastructure, dynamic cloud costs, lack of support from cloud providers, and unclear ownership of data costs. To address these challenges, companies can take several actions such as empowering data engineers with data observability platforms, making cost optimization a priority in their data engineering culture, leveraging automated root cause analysis for spend anomalies, setting up alerts for unexpected cost events, and scheduling regular financial reviews with relevant stakeholders.

Acceldata's integration with Databricks offers comprehensive operational observability for Apache Spark deployments, improving data quality and reliability at scale. The integration allows users to monitor their clusters and job performance, debug issues, perform root cause analysis (RCA) for failures, and enhance data reliability using Torch for Delta Lake. Deploying Acceldata involves installing an agent into Databricks, which hooks into Spark internals. Users can then understand their cluster and applications, monitor costs, debug applications, use alerts and logs to dig deeper into issues, and implement data reliability for Delta Lake.

Modern enterprises require a comprehensive, high-throughput, and low-latency real-time data feeds platform for operational intelligence. Kafka is beneficial for managing complex data pipelines and real-time data streams due to its ability to handle high-throughput data feeds with low latency times without occupying valuable computing resources. An optimal enterprise data observability solution should incorporate a Spark engine and treat Kafka as a first-class citizen with exclusive privileges, complementing Kafka's advanced data pipeline and analysis capabilities. Kafka can efficiently handle real-time data streams within milliseconds, manage continuously changing data using change data capture techniques, and work with microservices to handle complex data pipelines at scale. Combining Kafka with a multidimensional data observability solution like Acceldata allows businesses to make effective data-driven decisions in real-time by improving data quality, creating effective pipelines, automating processes, and analyzing data.

Modern enterprises require comprehensive, multi-layered data observability solutions due to the complex and fungible nature of their data needs. Data observability platforms help ensure reliable data, avoid data outages, manage data pipelines, and gain comprehensive visibility into an enterprise's data systems. A multi-dimensional approach is necessary for addressing a wide range of use cases and understanding data as multidimensional. Cloud environments require data observability to optimize complex data systems, technologies, and use cases from a single unified view. Acceldata Data Observability Platform helps enterprises monitor data pipelines, ensure data quality management, and manage data systems across the entire data life cycle. It also provides advanced capabilities such as automated anomaly detection, machine learning algorithms for problem-solving, and a 360-degree view of all data elements with relation to quality and operations.

The text discusses the author's career journey leading them to work at Acceldata, a company defining the data observability space by providing visibility needed for modern data infrastructure operations and scaling. The author highlights their experience with on-premises and cloud data technologies and how they founded dataSnight to help optimize investments in modern data stack technologies like Databricks and Snowflake. They emphasize the need for a comprehensive, multi-dimensional data observability solution that can operate across on-prem, cloud, and hybrid environments. The author is excited to be part of Acceldata's team delivering this solution to help enterprises manage their data operations more efficiently.

Service Level Agreements (SLAs) are crucial for organizations that rely heavily on technology services, especially those dealing with mission-critical business operations. Data reliability is a significant aspect of SLAs, ensuring the dependable and timely delivery of accurate data to applications and users. In today's complex data landscape, maintaining data reliability can be challenging due to larger and more fragile data pipelines. To eliminate unreliable data, businesses must create binding data reliability SLAs and use a modern data observability platform that provides insights into how their data operates. Key components of a data reliability SLA include defining service level indicators (SLIs) and objectives (SLOs), designating roles for data engineering and operations teams, and using the right data tools to track metrics effectively. The Acceldata Data Observability Platform offers a modern solution for ensuring data reliability by providing continuous, automated monitoring and predicting potential issues before they cause data incidents.