How to Modernize Data Lake Technologies with Cloud-Based Solutions

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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How to Overcome the Top Data Workflow Challenges

As the demand for data has grown, data workflows and processes have become increasingly complex. Consequently, the need for an efficient, modern data supply chain has never been more pressing. Yet, 451 Research’s latest survey shows that imbalances in the data supply chain could pose risks to data initiatives in today’s DataOps-driven world.
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Modern Data Analytics in the Cloud: Achieving an End-to-End Strategy

TDWI

Businesses today need fast, scalable, and agile data and analytics, and cloud-based solutions are proving critical to satisfying these requirements. They enable organizations to rapidly and easily spin up systems and services for collecting, managing, and analyzing data. More important, cloud-based solutions deliver value from “data gravity”the surging volumes of new data created in the cloud by social media, the IoT, multichannel customer behavior, and other activity.
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Fanatics Ingests Streaming Data to a Data Lake on AWS

Amazon Web Services

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. To accomplish this, Fanatics chose Attunity Replicate, a software solution featuring continuous data capture (CDC) and parallel threading for streaming data in real time from multiple sources into a data lake on Amazon S3. The data can then be consumed in Apache Kafka for real-time analytics. Attunity helps Fanatics avoid the heavy lifting of manually extracting data from disparate sources and enables the organization to see results in real time.
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Applying Hybrid Operational/Analytic Processing (Hoap) in the Real World

MariaDB

The need to monetize data has become a strategic imperative for businesses undergoing digital transformation, whether it’s using data to improve customer engagement, identify compelling opportunities or deliver actionable insight. However, as businesses look to expand revenue-generating, customer-facing applications beyond operational processing to include analytics, they find themselves outgrowing the their transactional database. They need full operational and full analytic capabilities.
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