You cannot manage what you cannot measure. Just as software engineers need a comprehensive view of application and infrastructure performance, data engineers need a comprehensive view of data system performance. In other words, data engineers need data observability.
Data observation can help data engineers and their organizations ensure the reliability of their data pipelines, gain insight into their data stack (including infrastructure, applications, and users), and identify, investigate, prevent, and remediate data problems. can solve. Data stewardship can help solve many common business data problems.
Data visualization can help solve scaling, optimization, and performance issues for data and analytics platforms by identifying operational bottlenecks. Data visualization can help prevent costs and resource overruns by providing operational visibility, guardrails and proactive alerts. And data observability can help prevent data quality and data outages by monitoring the reliability of data in pipelines and frequent changes.
Acceldata Data Observability Platform
Acceldata Data Observability Platform is an enterprise data observability platform for the modern data stack. The platform provides comprehensive visibility, giving data teams the real-time information they need to identify and prevent issues and make the data stack reliable.
Acceldata Data Observability Platform supports data sources such as Snowflake, Databricks, Hadoop, Amazon Athena, Amazon Redshift, Azure Data Lake, Google BigQuery, MySQL, and PostgreSQL. The Acceldata platform provides insights on:
- Calculation – Optimize the computing power, capacity, resources, cost and performance of your data infrastructure.
- Reliability – Improve data quality, reconcile and determine schedule deviations and data deviations.
- pipelines – Identify change issues, incidents, applications and provide alerts and insights.
- users – Real-time insights for Data Engineers, Data Scientists, Data Administrators, Platform Engineers, Data Officers and Platform Leads.
The Acceldata Data Observability Platform is built as a collection of microservices that work together to manage various business outcomes. It collects various metrics by reading and processing meta information from raw data as well as underlying data sources. It enables data engineers and data scientists to monitor computation performance and validate data quality policies defined within the system.
Acceldata’s data reliability monitoring platform allows you to set a variety of policies to ensure that the data in your pipelines and databases meets required quality levels and is reliable. Acceldata’s compute performance platform displays all compute costs incurred on a customer’s infrastructure, and allows you to set budgets and configure alerts when budgets are hit.
The Acceldata Data Observability Platform architecture is divided into the data plane and the control plane.
The data plane of the Acceldata platform connects to underlying databases or data sources. It never saves the data and returns metadata and results to the control plane, which receives and stores the results of the run. Data analyzers, query analyzers, crawlers, and the Spark infrastructure are part of the data plane.
Data source integration comes with a microservice that crawls the metadata of the data sources from their underlying metastore. Each profiling, policy execution, and sample data job is converted into a Spark job by the analyst. Job execution is managed by the Spark cluster.
The control plane is the orchestrator of the platform and is accessed through a UI and API interface. The control plane stores all metadata, profiling data, job results, and other data in the database layer. It manages the data plane and sends requests to run tasks and other tasks if necessary.
The Data Computation Monitoring portion of the platform receives metadata from external sources via REST APIs, aggregates it on the Data Collector server, and then publishes it to the Data Ingestion module. Agents deployed near data sources regularly collect metrics before publishing them to the data ingest module.
The database layer, which includes databases such as Postgres, Elasticsearch, and VictoriaMetrics, stores data collected from agents and data control servers. The Data Processing Server facilitates the correlation of data collected by the Agents and the Data Collection Service. Dashboard Server, Agent Control Server and Management Server are data computation infrastructure services.
When a major event (errors, warnings) occurs in a system or subsystem monitored by the platform, it is either displayed on the user interface or communicated to the user via information channels such as Slack or email and is communicated to the platform Alert and information servers are used. ,
Find issues early in the data pipeline to isolate them before they reach the warehouse and affect downstream analytics:
- Shift to links to files and streams: Perform reliability analysis in the “raw landing zone” and “rich zone” before the data reaches the “consumption zone” to avoid wasting valuable cloud credits and making poor decisions due to bad data.
- Data reliability powered by Spark: Thoroughly inspect and identify issues at petabyte scale with the power of open-source Apache Spark.
- Cross-data-source tuning: Run reliability checks that aggregate different streams, databases, and files to ensure correctness across migrations and complex pipelines.
Get multilevel operational insights to quickly resolve data issues:
- Know why, not just when: Delays in the debug data route by correlating data and computation spikes.
- Find the true cost of bad data: Point to the money wasted on computing on unreliable data.
- Optimize data pipelines: Whether drag-and-drop or code-based, single platform or polyglot, you can diagnose data pipeline failures in one place and across all layers of the stack.
Maintain a continuous, comprehensive overview of workloads and quickly identify and resolve issues through the Operations Control Center:
- Built by data experts for data teams: Custom alerts, audits, and reports for today’s leading cloud data platforms.
- Accurate spend information: Forecast costs and control usage to maximize ROI, even as platforms and prices evolve.
- One Window: Budget and monitor all your cloud data platforms in one view.
Full data coverage with flexible automation:
- Fully automated reliability checking: Quickly find out about missing, late or incorrect data on thousands of tables. Add advanced data drift alerts with one click.
- Reusable SQL and User Defined Functions (UDFs): Express domain-oriented reusable reliability checking in five programming languages. Apply segmentation to understand the reliability of all dimensions.
- Comprehensive data source coverage: Enforce enterprise data reliability standards across your business, from modern cloud data platforms to traditional databases and complex files.
Acceledata’s Data Observability Platform works across technologies and environments, providing enterprise data observability for the modern data stack. For Snowflake and Databricks, ExcelData can help maximize return on investment by providing insights into performance, data quality, cost and more. Visit www.acceldata.io for more information.
Ashwin Rajeev is the co-founder and CTO of Acceldata.
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