Published By: Attunity
Published Date: Jan 14, 2019
This whitepaper explores how to automate your data lake pipeline to address common challenges including how to prevent data lakes from devolving into useless data swamps and how to deliver analytics-ready data via automation.
Read Increase Data Lake ROI with Streaming Data Pipelines to learn about:
• Common data lake origins and challenges including integrating diverse data from multiple data source platforms, including lakes on premises and in the cloud.
• Delivering real-time integration, with change data capture (CDC) technology that integrates live transactions with the data lake.
• Rethinking the data lake with multi-stage methodology, continuous data ingestion and merging processes that assemble a historical data store.
• Leveraging a scalable and autonomous streaming data pipeline to deliver analytics-ready data sets for better business insights.
Read this Attunity whitepaper now to get ahead on your data lake strategy in 2019.
Data is the fuel driving rapid innovation powered by artifi cial intelligence. Enterprises
need modern data platform purpose-built for machine learning, accelerating insight while
simplifying complex data pipelines in analytics.
Published By: StreamSets
Published Date: Sep 24, 2018
If you’ve ever built real-time data pipelines or streaming applications, you know how useful the Apache Kafka™ distributed streaming platform can be. Then again, you’ve also probably bumped up against the challenges of working with Kafka.
If you’re new to Kafka, or ready to simplify your implementation, we present common challenges you may be facing and five ways that StreamSets can make your efforts much more efficient and reliable
FREE O'REILLY EBOOK: BUILDING REAL-TIME DATA PIPELINES Unifying Applications and Analytics with In-Memory Architectures You'll Learn:
- How to use Apache Kafka and Spark to build real-time data pipelines - How to use in-memory database management systems for real-time analytics
- Top architectures for transitioning from data silos to real-time processing
- Steps for getting to real-time operational systems - Considerations for choosing the best deployment option
The Path to Predictive Analytics and Machine Learning This Ebook will be your guide to building and deploying scalable, production-ready machine-learning applications. Inside, you will find several machine learning use cases, code samples to help you get started, and recommended data processing architectures.
Pairing Apache Kafka with a Real-Time Database Learn how to:
? Scope data pipelines all the way from ingest to applications and analytics
? Build data pipelines using a new SQL command: CREATE PIPELINE ? Achieve exactly-once semantics with native pipelines
? Overcome top challenges of real-time data management
Traditional data processing infrastructures—especially those that support applications—weren’t designed for our mobile, streaming, and online world. However, some organizations today are building real-time data pipelines and using machine learning to improve active operations.
Learn how to make sense of every format of log data, from security to infrastructure and application monitoring, with IT Operational Analytics--enabling you to reduce operational risks and quickly adapt to changing business conditions.
Published By: SugarCRM
Published Date: Apr 08, 2014
CRM has long been seen as a must-have sales tool. However, much of the value of traditional CRM accrues to managers, not the reps that use them daily. Learn how CRM designed for the individual benefits the entire sales organization from increased data quality to more predictable revenue pipelines.
Published By: Attunity
Published Date: Nov 15, 2018
IT departments today face serious data integration hurdles when adopting and managing a Hadoop-based data lake. Many lack the ETL and Hadoop coding skills required to replicate data across these large environments. In this whitepaper, learn how you can provide automated Data Lake pipelines that accelerate and streamline your data lake ingestion efforts, enabling IT to deliver more data, ready for agile analytics, to the business.
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