![]() ![]() The dashboard server, agent control server, and management server are the data computation monitoring infrastructure services. The data processing server facilitates the correlation of data collected by the agents and the data collector service. The database layer, which includes databases like Postgres, Elasticsearch, and VictoriaMetrics, stores the data collected from the agents and data control server. The agents deployed near the data sources collect metrics regularly before publishing them to the data ingestion module. The platform’s data computation monitoring section obtains the metadata from external sources via REST APIs, collects it on the data collector server, and then publishes it to the data ingestion module. It manages the data plane and, as needed, sends requests for job execution and other tasks. The control plane stores all metadata, profiling data, job results, and other data in the database layer. The control plane is the platform’s orchestrator, and is accessible via UI and API interfaces. The execution of jobs is managed by the Spark clusters. Any profiling, policy execution, and sample data task is converted into a Spark job by the analyzer. The data analyzer, query analyzer, crawlers, and Spark infrastructure are a part of the data plane.ĭata source integration comes with a microservice that crawls the metadata for the data source from their underlying meta store. It never stores any data and returns metadata and results to the control plane, which receives and stores the results of the executions. The data plane of the Acceldata platform connects to the underlying databases or data sources. The Acceldata Data Observability Platform architecture is divided into a data plane and a control plane. Acceldata’s compute performance platform displays all of the computation costs incurred on customer infrastructure, and allows you to set budgets and configure alerts when expenditures reach the budget. It allows data engineers and data scientists to monitor compute performance and validate data quality policies defined within the system.Īcceldata’s data reliability monitoring platform allows you to set various types of policies to ensure that the data in your pipelines and databases meet the required quality levels and are reliable. It gathers various metrics by reading and processing raw data as well as meta information from underlying data sources. The Acceldata Data Observability Platform is built as a collection of microservices that work together to manage various business outcomes. Users - Real-time insights for data engineers, data scientists, data administrators, platform engineers, data officers, and platform leads.Pipelines - Identify issues with transformation, events, applications, and deliver alerts and insights.Reliability - Improve data quality, reconciliation, and determine schema drift and data drift.Compute - Optimize compute, capacity, resources, costs, and performance of your data infrastructure.The Acceldata platform provides insights into: ![]() It platform provides comprehensive visibility, giving data teams the real-time information they need to identify and prevent issues and make data stacks reliable.Īcceldata Data Observability Platform supports data sources such as Snowflake, Databricks, Hadoop, Amazon Athena, Amazon Redshift, Azure Data Lake, Google BigQuery, MySQL, and PostgreSQL. Acceldata Data Observability PlatformĪcceldata Data Observability Platform is an enterprise data observability platform for the modern data stack. And data observability can help prevent data quality and data outages, by monitoring data reliability across pipelines and frequent transformations. Data observability can help avoid cost and resource overruns, by providing operational visibility, guardrails, and proactive alerts. ĭata observability can help resolve data and analytics platform scaling, optimization, and performance issues, by identifying operational bottlenecks. Data observability can help solve all kinds of common enterprise data issues. In other words, data engineers need data observability.ĭata observability can help data engineers and their organizations ensure the reliability of their data pipelines, gain visibility into their data stacks (including infrastructure, applications, and users), and identify, investigate, prevent, and remediate data issues. Just as software engineers need a comprehensive picture of the performance of applications and infrastructure, data engineers need a comprehensive picture of the performance of data systems.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |