How to Modernize Data Lake Technologies with Cloud-Based Solutions

tdwi.org

Data lakes based on Hadoop technologies have proved themselves valuable in mission-critical use cases such as data warehousing, advanced analytics, multichannel marketing, complete customer views, digital supply chains, and the modernization of data management.Most Hadoop users are committed to the data lake method of managing data, but they are limited by Hadoop shortcomings in key areas such as cluster maintenance, administration cost, resource management, metadata management, and support for SQL and other relational technologies. Many view cloud-based solutions as the optimal replacement for their data lake, but they are not ready to make such a significant change. The truth is: they don't have to, as the two technologies can coexist.
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Spotlight

Ritika Gunnar, Vice President, Offering Management, Data and Analytics at IBM, provides an introduction to a new data platform designed to make data simple and accessible to all organizations - a platform for the cognitive business.

OTHER ON-DEMAND WEBINARS

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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Riding the Data Privacy Wave: How Will You Stay Afloat?

IBM

Data privacy regulation is bigger than just GDPR. Other countries and jurisdictions are enacting their own versions of the data privacy regulation, each with subtle nuances - such as the California Consumer Privacy Act (CCPA), Lei Geral de Proteção de Dados (LGPD), and more - and that’s on top of existing privacy regulations. Moreover, consumers increasingly expect more protection for their sensitive information. A recent IBM-Harris poll of 10,000 individuals revealed that 75% of consumers won’t buy from companies they don’t trust no matter how great their product or service.
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Fanatics Ingests Streaming Data to a Data Lake on AWS

awscloud.com

Fanatics, a popular sports apparel website and fan gear merchandiser, needed to ingest terabytes of data from multiple historical and streaming sources transactional, e-commerce, and back-office systems to a data lake on Amazon S3. Once ingested, the data would be analyzed to better identify, predict, and fulfill customer needs related to the products Fanatics offers in over 300 online and offline stores.
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Improving Transactional Applications with Analytics

MariaDB

Today, most web and mobile applications are limited to “lightweight” analytics because general-purpose databases can be optimized for transactional or analytical workloads, but not both – and since transactional processing is critical, applications have to compromise on analytics. However, what if an e-commerce application could let customers know which products are soon to be sold out based on clickstream data, shopping carts, current inventory and recent purchases as well as historical buying patterns and emerging shopping trends? In this webinar, attendees will learn how to leverage MariaDB ColumnStore to provide transactional applications with real-time analytics on historical data.
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Spotlight

Ritika Gunnar, Vice President, Offering Management, Data and Analytics at IBM, provides an introduction to a new data platform designed to make data simple and accessible to all organizations - a platform for the cognitive business.

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