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
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
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
Published By: Datastax
Published Date: May 20, 2019
DataStax Enterprise and Apache Kafka are designed specifically to fit the needs of modern, next-generation businesses. With DataStax Enterprise (DSE) providing the blazing fast, highly-available hybrid cloud data layer and Apache Kafka™ detangling the web of complex architectures via its distributed streaming attributes, these two form a perfect match for event-driven enterprise architectures.
Published By: Attunity
Published Date: Nov 15, 2018
With the opportunity to leverage new analytic systems for Big Data and Cloud, companies are looking for ways to deliver live SAP data to platforms such as Hadoop, Kafka, and the Cloud in real-time. However, making live production SAP data seamlessly available wherever needed across diverse platforms and hybrid environments often proves a challenge.
Download this paper to learn how Attunity Replicate’s simple, real-time data replication and ingest solution can empower your team to meet fast-changing business requirements in an agile fashion. Our universal SAP data availability solution for analytics supports decisions to improve operations, optimize customer service, and enable companies to compete more effectively.
How can you open your analytics program to all
types of programming languages and all levels of
users? And how can you ensure consistency across
your models and your resulting actions no matter
where they initiate in the company?
With today’s analytics technologies, the conversation
about open analytics and commerical analytics is no
longer an either/or discussion. You can now combine
the benefits of SAS and open source analytics
technology systems within your organization.
As we think about the entire analytics life cycle, it’s
important to consider data preparation, deployment,
performance, scalability and governance, in addition
to algorithms. Within that cycle, there’s a role for
open source and commercial analytics.
For example, machine learning algorithms can
be developed in SAS or Python, then deployed in
real-time data streams within SAS Event Stream
Processing, while also integrating with open systems
through Java and C APIs, RESTful web services,
Apache Kafka, HDFS and more.
Published By: Attunity
Published Date: Feb 12, 2019
Read this technical whitepaper to learn how data architects and DBAs can avoid the struggle of complex scripting for Kafka in modern data environments. You’ll also gain tips on how to avoid the time-consuming hassle of manually configuring data producers and data type conversions. Specifically, this paper will guide you on how to overcome these challenges by leveraging innovative technology such as Attunity Replicate. The solution can easily integrate source metadata and schema changes for automated configuration real-time data feeds and best practices.
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