Faster AI Deployment on Hadoop and Spark with RapidMiner and Microsoft Azure HDInsight

Leveraging business data as a valuable asset is no longer a debated concept – it’s a broadly adopted, competitive undertaking that’s part and parcel to cloud modernization. Today, if there’s one thing that defines competitive advantage in the data analytics arena it’s streaming data platforms. Older approaches employing batch-only analytics, brittle ETL pipelines, and the latency they can introduce just don’t cut it anymore. Cloud-Modernized Analytics are poised to step in and take over.

The results are in! Over the summer of 2021, Odaseva and Forrester collaborated on a survey studying how CRM professionals in large enterprises feel about their ability to manage data, the challenges they are facing, and the emerging trends impacting their success. The responses were eye-opening.

GDPR affects companies in the United States and Canada. Failure to comply with the GDPR could result in fines of up to $20 million or higher. Join us to learn: - What is the GDPR and severe consequences of non-compliance - What are the eight main GDPR requirements - How ready is your company and what you can do now

Modern-day systems are transforming into complex, open-source, cloud-native services running on various environments and being developed/deployed at lightning speed by distributed teams. When working on these systems, identifying a broken link in the chain can be near impossible. Everything fails at one point or another, whether due to code bugs, infrastructure overload, or changes in end-user behavior or market driven factors or errors in data collection. This has led to the rise of DataOps with a focus on changing the organizational speed and trust in delivering data pipelines and the related artifacts by co-creating “decision quality” data with the consumers. This development has led to the idea of observability that includes monitoring, tracking, and triaging incidents to prevent downtime of the systems and around several factors such as freshness, distribution, volume, schema, lineage.