Data Observability / DataOps using AI

Data Observability / DataOps using AI
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.
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Spotlight

For centuries, patients have sought medical help for their ailments. Just as in the past, however, there are still many illnesses – both wellknown, widespread diseases and rare conditions – that initially cause few or inconclusive symptoms, and many patients leave the doctor’s office with an incorrect diagnosis. In addition, diseases may progress slowly or quickly depending on the individual.

OTHER ON-DEMAND WEBINARS

Experience limitless analytics with Azure Synapse Analytics

View this webinar where our experts discussed the new era of analytics with the Microsoft Azure Synapse Analytics platform. It is a limitless analytics service with unmatched time to insight that bring together data integration, enterprise data warehousing and big data analytics – all into a single service.
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Building the Road Map for Real-Time Data and Analytics

As organizations strive to be more competitive, they often need real-time insights; no one wants to make decisions based on stale data. TDWI research indicates that real-time data collection is already in the mainstream. Some use cases include inventory management, fulfillment, supply chain, and logistics in which retailers must be able to assess product availability and consumer demand in real time. Forward-looking organizations also want to enrich real-time data with other data types to provide even better analytics.
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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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Data Privacy in Analytics

Data Protection and Privacy Compliance are becoming more ubiquitous and gaining in importance. In recent years, legislation has reacted by introducing e.g. the GDPR, the UK Data Protection Law or the California Consumer Privacy Act and associated regulations (“CCPA”). European Regulators especially have been clear that they take data privacy of their citizens very seriously, as shown in the January 2022 ruling of the Austrian courts against a website that was using Google Analytics.
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Spotlight

For centuries, patients have sought medical help for their ailments. Just as in the past, however, there are still many illnesses – both wellknown, widespread diseases and rare conditions – that initially cause few or inconclusive symptoms, and many patients leave the doctor’s office with an incorrect diagnosis. In addition, diseases may progress slowly or quickly depending on the individual.

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