Systems | Development | Analytics | API | Testing

In this webinar, Unravel CDO and VP Engineering Sandeep Uttamchandani describes the start of any large, data-driven project: the Discover phase. You must identify the insights you want to generate from the project, you must discover; that is, you must identify the current data assets you have, and the new data assets you will need, to generate the insights you want to produce. Sandeep expertly guides you through this process, and shows you how to invest the right amount of time and effort to get the job done.

In this webinar, Unravel CDO and VP Engineering Sandeep Uttamchandani describes the second step for any large, data-driven project: the Prep phase. Having found the data you need in the Discover phase, it's time to get your data ready. You must structure, clean, enrich, and validate static data, and ensure that "live," updated or streamed data events are continually ready for processing.

In this webinar, Unravel CDO and VP Engineering Sandeep Uttamchandani describes the third step for any large, data-driven project: the Build phase. You've found your data, in the Discover phase, and readied it for processing, in the Prep phase. Now you need to Build the logic that will actually process the data and the machine learning models that the data will be run through.

In this webinar, Unravel CDO and VP Engineering Sandeep Uttamchandani describes the fourth and final step for any large, data-driven project: the Operationalize phase. You've found your data (Discover phase), readied it for processing (Prep phase), and built out your processing logic and machine learning model(s) (Build phase). Now you need to Operationalize all your work to data as a live project, in production.

Join Chris Santiago, Solutions Engineer Director at Unravel Data, as he takes you through Unravel's approach to getting better and finer grain visibility with Spark applications and how to tune and optimize them for resource efficiency. An overview of out of the box tools like Ganglia and their overall lack of visibility on Databricks jobs How Unravel helps you gain finer grain visibility, observability, monitoring into Spark data pipelines How Unravel can recommend better configurations and tuning of Spark applications.

Kafka & Spark data pipelines are ubiquitous in any modern data stack. Developing Spark and Kafka applications have become simpler over the years but operating them in production environments still remains challenging to say the least.

Organizations are moving big data from on-premises to the cloud, using best-of-breed technologies like Databricks, Amazon EMR, Azure HDI, and Cloudera, to name a few. However, many cloud migrations fail. Why? And, how can you overcome the barriers and succeed? Join Chris Santiago, Director of Solution Engineering, as he describes the biggest pain points and how you can avoid them, and make your move to the cloud a success.

Behind every successful insight (BI analytics or ML model) is a reliable data pipeline! These pipelines are planned, implemented, deployed, and monitored in an ongoing fashion referred to as the DataOps infinity loop (similar to CI/CD for traditional software). This talk covers battle scars in managing DataOps at scale, and how building checkpoints in the DataOps loop can reduce missed SLAs, cost outages, escalation from data users, and most importantly avoid data pipeline surprises!

How their tactical/strategic focus areas are evolving in these challenging times Cloud big data migration strategy, do's and don'ts Practical advice they can share for other leaders in the big data community How Unravel has helped DBS optimize their big data

Amazon EMR is a go-to platform for those who want all the power of Hadoop and Spark in the cloud. However, cost and performance trade-offs can reduce the advantages of EMR over alternatives. Lack of visibility into the root cause of problems, right-sizing options, and cost allocation can add confusion and frustration for EMR users. Unravel Data gives you visibility into the minute-to-minute operations of your workloads on EMR. Get root cause analysis (RCA) of workload breakdowns and slowdowns; AI-powered recommendations; and proactive fixes for many problems. With Unravel Data, you can meet and beat your SLAs, saving thousands - even millions - of dollars per year in the process.