The IBM Cloudant Product Report, developed with TrustRadius based on user reviews, showcases why TrustRadius designated Cloudant as a “Top Rated Product” in its NoSQL Databases category. You’ll learn why users chose the Cloudant JSON doc store, built on open source Apache® CouchDB™, to develop their scalable and always-on web, mobile and Internet of Things apps, through reviews like these:

• “Replication: powerful and easy DevOps function to save, copy or back up the data from one Cloudant database to another”
• “[Cloudant] allows us to scale our growth in the terms of transactions without too many headaches”
• “Switching to Cloudant meant my administration time went from two hours to about five minutes per week”

The IBM Cloudant Product Report will also explain why Cloudant is ideal for apps that perform offline, and how its built-in geospatial querying and integrated connectors to analytics tools make it purpose-built for developers bringing new data-driven capabilities to their apps.

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The potent combination of NoSQL databases, like IBM Cloudant, and Apache Spark provide developers and data scientists with access to rapid integration, transformation and analysis of all kinds of business data.

This free ebook, "Getting Faster Insights from Application Data with IBM Cloudant and Apache Spark," explains how to deploy the open source Spark-Cloudant connector to conduct in-memory analytics of your Cloudant JSON data in a Jupyter Notebook. Through examples, this eBook showcases:

• How one company used Cloudant and Spark to conduct smarter, real-time maintenance of its assets using connected sensors
• How Notebooks offer a powerful back-end interface for data science and analysis, while displaying text and annotations, code and data, and visualizations, all in the same environment
• How to get started with analytics on your own JSON data while tapping into additional features of Cloudant, such as offline data access and native support for GeoJSON

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There are many types of databases and data analysis tools to choose from when building your application. Should you use a relational database? How about a key-value store? Maybe a document database? Is a graph database the right one? What about polyglot persistence and the need for advanced analytics?

If you feel a bit overwhelmed, don’t worry. This guide lays out the various database options and analytic solutions available to meet your app’s unique needs. You’ll see how data can move across databases and development languages, so you can work in your favorite environment without the friction and productivity loss of the past.

This field guide will help you answer:

• What are the defining characteristics and strengths of today’s different cloud databases?
• How can data in NoSQL stores be analyzed to learn more about your customers?
• How are different open source databases used together to achieve polyglot persistence?

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A big data integration platform that is flexible and scalable is needed to keep up with today’s ever-increasing big data volume. IBM's information integration platform is ready to scale and perform with the ever-changing amount and types of data.

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Today data volumes are exploding in every facet of our lives. Business leaders are eager to harness the power of big data but before setting out into the big data world it is important to understand that as opportunities increase ensuring that source information is trustworthy and protected becomes exponentially more difficult. This paper provides a detailed review of the best practices clients should consider before embarking on their big data integration projects.

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This white paper presents seven essential elements for high-performance information integration and how they apply to business and technical decision makers responsible for designing, building, supporting and using scalable data processing systems.

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Enterprise data warehouses must take action now to modernize the traditional data warehouse.

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Companies face challenges with managing their data and need strategies and solutions to turn midmarket data into a working asset.

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In this survey we investigate the current state of the data warehouse and examine its recent challengers.

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Can your database systems handle data growth and keep up with performance requirements? Here are six reasons to change.

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