Streaming Analytics with DataOps

Theย technical architectureย that powersย steaming analyticsย enables terabytes of data to flow through the enterpriseโ€™s data pipelines. Real-time analytics require real-time updates to data. To that end, data must be continuously integrated, cleaned, preprocessed, transformed and loaded in a data warehouse or other database that is architected to meet the required response time and load.

IDC predicts that the total amount of data worldwide will grow to 175 Zettabytes by 2025 โ€“ thatโ€™s 10X growth in the past several years. Nearly 30% of the worldโ€™s data will need real-time processing. From our daily conversations with leaders of data teams and organizations, we find that many enterprises are scrambling to prepare. Streaming analytics will need to use a management method calledย DataOpsย to cope with the data tsunami. DataOps will help enterprises overcome four major challenges in streaming analytics.ย 

Challenge #1: Data Errors

Data is notoriously dirty. Terabytes of data flow through the enterprise continuously. If one wrong value enters the data pipeline, it couldย corrupt analytics. You wouldnโ€™t want a machine-learning algorithm to send snow shovels to Arizona because your weather data supplier expressed the temperature in centigrade. That may be a silly example, but a real error might be subtle and extremely difficult to catch, given the sheer quantity of data that a typical enterprise consumes.ย 

DataOpsย orchestratesย tests across all data pipelines to catch errors before they reach analytics. Every processing and transformation stage of the enterpriseโ€™s numerous data pipelines are validated using input, output and business logic tests. If an error occurs, the data team is alerted. If the error is critical, the data pipeline temporarily stops, so the problem can be investigated. Catching these errors as early as possible is an effective way to reduce unplanned work and avoid displaying inaccurate data to users.

Challenge #2: Event-Driven Processing

ย DataOps orchestration can also automate event-driven processing typical of streaming applications. The testing and filtering of data in DataOps provides transparency into real-time data streams. Tests, filters and orchestrations can be linked to messages and streaming events.

Challenge #3: Manual Processes

Data scientists are a precious and expensive resource. Yet, they spend the majority of their time executing manual steps to prepare and process data. DataOps automates the flow of data from data sources to published analytics. In a DataOps enterprise, automated orchestration extracts data from critical operational systems and loads it into a data lake, under the control of the data team. To comply with data governance, data can be deidentified or filtered. The orchestration engine thenย prepsย the data and transfers it into centralized data warehouses and makes it available to self-service tools for decentralized analysis. Without manual processes, data can flow continuously, efficiently and robustly. Data scientists are freed to create new analytics that drive growth.

Challenge #4: Cycle Time

Most organizations suffer from an unacceptably lengthyย cycle time, i.e., the time that it takes to turn an idea into production analytics. For example, many organizations report to us that it takes them months to make a simple change. DataOps uses virtual workspaces to ease the transition from development to production and it borrows from DevOps (orchestrated tests and deployment/delivery) to reduce cycle time from months/weeks to days/hours.ย 

Conclusion

DataOps brings the IT team, data engineers, data scientists and business users together into aย coherent set of workflows. It automates data operations and enforces data filters that catch errors in real-time. It reduces cycle time by minimizing unplanned work and creating aย continuous deliveryย framework.

The transformation of raw data to analytics insights has become a point of differentiation among enterprises. Tomorrowโ€™s best-run organizations will attain market leadership on a foundation of efficient and robust management of data. Coping with massive amounts of data will require everyone working together and using DataOps automation to enforce data quality, parallel development and the fastest possible cycle time.

Sign-Up for our Newsletter

Get the latest straight into your inbox

Open Source Data Observability Software

DataOps Observability: Monitor every Data Journey in an enterprise, from source to customer value, and find errors fast! [Open Source, Enterprise]

DataOps Data Quality TestGen: Simple, Fast Data Quality Test Generation and Execution. Trust, but verify your data! [Open Source, Enterprise]

DataOps Software

DataOps Automation: Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change. [Enterprise]

recipes for dataops success

DataKitchen Consulting Services


Assessments

Identify obstacles to remove and opportunities to grow

DataOps Consulting, Coaching, and Transformation

Deliver faster and eliminate errors

DataOps Training

Educate, align, and mobilize

Commercial Pharma Agile Data Warehouse

Get trusted data and fast changes from your warehouse

 

dataops-cookbook-download

DataOps Learning and Background Resources


DataOps Journey FAQ
DataOps Observability basics
Data Journey Manifesto
Why it matters!
DataOps FAQ
All the basics of DataOps
DataOps 101 Training
Get certified in DataOps
Maturity Model Assessment
Assess your DataOps Readiness
DataOps Manifesto
Thirty thousand signatures can't be wrong!

 

DataKitchen Basics


About DataKitchen

All the basics on DataKitchen

DataKitchen Team

Who we are; Why we are the DataOps experts

Careers

Come join us!

Contact

How to connect with DataKitchen

 

DataKitchen News


Newsroom

Hear the latest from DataKitchen

Events

See DataKitchen live!

Partners

See how partners are using our Products

 

Monitor every Data Journey in an enterprise, from source to customer value, in development and production.

Simple, Fast Data Quality Test Generation and Execution. Your Data Journey starts with verifying that you can trust your data.

Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change.