Data lakes, such as those powered by Hadoop, are an excellent choice for analytics and reporting at scale. Hadoop scales horizontally and cost-effectively and fulfills long-running operations spanning big data sets. However, the continual growth of real-time analytics requirements — where operations need to be completed in seconds rather than minutes, or milliseconds rather than seconds — has brought new challenges to Hadoop based solutions.

MySQL^® is arguably the most widely used open source database in the world, even if you don't include MariaDB^® or Percona^® as a variant. But it has a host of challenges and options to solve them.

Learn how companies have added speed and scale to MySQL deployments for different use cases. This webinar will cover the various options available and when each option makes sense. It will also cover how to evolve your architecture over time to add the speed, scale, agility and new technologies needed for digital transformation and other initiatives.

As an in-memory computing platform, GridGain® and Apache Ignite support native persistence that stores data and indexes transparently on non-volatile memory, SSD or disk. When persistence is enabled, memory becomes a cache for the most frequently used data and indexes. Native persistence is ACID-compliant, durable and enables immediate availability on a restart of each node. Data is never lost; GridGain supports full and incremental snapshots along with continuous archiving, and provides Point-in-Time recovery to an individual transaction.

PostgreSQL is one of the most widely databases globally, especially if you add up all the different distributions. This is in part what makes it challenging to figure out how to lower latency and improve scalability with business-critical PostgreSQL deployments.

Guaranteeing that your in-memory computing solution stays up and running is the most important goal for a rolling out a new production environment. The trick is making sure that you have all the bases covered and have thought through all your requirements, needs, and potential roadblocks.

In this webinar, Apache^® Ignite™ PMC Chair Denis Magda shares a checklist to consider for your Apache Ignite production deployments. This checklist includes:

With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data, and hours to train models. Learn how Apache Ignite eliminates runs model training and execution in near-real-time and makes continuous learning possible.

In this Webinar, Yuri Babak, the head of ML/DL framework development at GridGain and major contributor to Apache Ignite, will explain how ML and DL work with Apache Ignite, and how to get started. Topics include:

The Oracle^® Database is one of the most scalable RDBMSs on the market. But even Oracle has been pushed beyond its architectural limits by new business needs and software layers. The reason is simple: the performance issues cannot be solved by making changes to the database.

Digital transformation is arguably the most important initiative in IT today, in large part because of its ability to improve the customer experience and business operations, and to make a business more agile.  

But delivering a responsive digital business is not possible at scale without in-memory computing. This session, the third in the In-Memory Computing Best Practices Series, dives into how in-memory computing acts as a foundation for digital business.  Topics include how in-memory computing is used to:

It's hard to improve the customer experience when your existing applications can't handle the existing loads and are inflexible to change. This webinar is Part 2 in our In-Memory Computing Best Practices Series. It focuses on the most common first in-memory computing project, adding speed and scale to existing applications.