Visualizing Asset Protection in a Big Data World

infogix

Data breaches are in the news nearly every day as companies struggle to protect their customers’ greatest assets  their personal information. From the theft of credit card information and personal health records to critical business information leakage; the threats are constant. Certainly, firewalls and endpoint protection are critical and necessary, but what about data controls and analytics? In this one hour webinar you will hear the latest on how companies are leveraging end-to-end controls, monitoring an analytics solutions to meet information privacy and security compliance standards. Even better, you will hear directly from Kemper, a leading insurer with $8B in assets, and learn how they are approaching information security risk management.
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Spotlight

The concept of Big Data as a Service (BDaaS) has emerged as an option in the past several years, as public cloud providers have introduced Hadoop as a Service and Spark as a Service offerings running on cloud-based infrastructure. As in the Infrastructure as a Service (IaaS) market, the cloud operating model of self-service and elasticity is compelling to many organizations deploying Big Data applications. However, until now, the benefits of BDaaS weren’t available for on-premises deployments of Hadoop, Spark, and other Big Data workloads.

OTHER ON-DEMAND WEBINARS

Overcoming the Obstacles for Data Lake Success

Business users have a tremendous appetite for data. The “single version of the truth” was a rallying cry to deliver business data in data warehouses for years. Users were able to digest and analyze large volumes of corporate data. They reviewed trends, identified anomalies, and supported decision-making because they had the detailed data to support action. As the business/data environment matured, the need for more diverse detail and increased delivery speed only grew. The data lake became a successful mechanism for delivering data from diverse systems in a timely manner.
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Journey to the Cloud, Self-Service BI, and Data Lakes with Data Virtualization

denodo

Denodo DataFest, the premier agile data management and analytics conference, returned once again to New York and London in 2018. Our annual user conference brings together industry analysts, subject matter experts, business leaders, data management leaders to discuss data strategies to enable a successful journey to the cloud, self-service BI, and data lakes with data virtualization. The event invited attendees to watch demos, hear customer success stories, and educate themselves on the best practice implementations of data virtualization.
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Streaming Data: The Nexus of Cloud-Modernized Analytics

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.
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Strategies for Fitting a Data Lake into a Modern Data Architecture

McKnight Consulting Group

Whether to take data ingestion cycles off the ETL tool and the Data Warehouse or to facilitate competitive Data Science and building algorithms in the organization, the Data Lake a place for unmodeled and vast data will be provisioned widely in 2019. Though it doesn’t have to be complicated, the Data Lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the Data Swamp, but not the Data Lake! The tool ecosystem is building up around the Data Lake and soon many will have a robust Lake and Data Warehouse. We will discuss policy to keep them straight, send “horses to courses,” and keep up users’ confidence in the Data Platforms.
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Spotlight

The concept of Big Data as a Service (BDaaS) has emerged as an option in the past several years, as public cloud providers have introduced Hadoop as a Service and Spark as a Service offerings running on cloud-based infrastructure. As in the Infrastructure as a Service (IaaS) market, the cloud operating model of self-service and elasticity is compelling to many organizations deploying Big Data applications. However, until now, the benefits of BDaaS weren’t available for on-premises deployments of Hadoop, Spark, and other Big Data workloads.

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