Improving Speed, Scalability, and the Customer Experience with In
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WHITE PAPER Improving Speed, Scalability, and the Customer Experience with In-Memory Data Grids A GridGain Systems In-Memory Computing White Paper Improving Speed, Scalability, and the Customer Experience with In-Memory Data Grids WHITE PAPER One of the biggest challenges for customer- and partner-fac- THE CHALLENGES WITH EXISTING ing applications is keeping up with ever-increasing database AND NEW APPLICATIONS, APIS, AND loads. The 10-1000 times growth in data transactions, que- ANALYTICS ries, and size over the last decade has overwhelmed many databases. The most common solutions are vertically scaling The most common reason for the exponentially growing writes with expensive hardware and handling query loads loads on existing applications is digital transformation. The with replication. But both solutions have their limitations. reason for these increased loads is readily apparent. Consum- ers demand a faster, more convenient customer experience More and more, companies are instead using in-memory anytime, anywhere, on any device. When consumers do get data grids (IMDG) or broader in-memory computing (IMC) this new experience, they use it to access data much more platforms as a solution. An IMDG adds in-memory speed frequently But delivering this experience requires a combina- and horizontal scale to existing applications. This solution tion of real-time data, analytics, and automation. slides in-between the applications and their databases, and keeps all the data in-memory and in-sync with the data- Delivering an improved, more-digital customer experience is base. An IMC platform combines the capabilities of an IMDG critical to business survival. Consumers will quickly abandon with in-memory database (IMDB), streaming analytics, and a vendor to get a better experience someplace else. An Aber- machine and deep learning. It allows the same technology deen study found that companies with a strong omnichannel and data to be reused across projects. engagement retained 89 percent of their customers. Those with a weak strategy retained 33 percent. Digital transfor- Many companies first adopted an IMDG to fix a point per- mation has spread beyond sales and services to all parts of formance and scalability problem with a single application. a company. It also helps improve core operations and supply But over time, these companies ended up extending their chain efficiency, by leveraging the Internet of Things (IoT), new IMDG and IMC platforms to create a new real-time data for example. layer for customer facing applications. IMC not only provided the speed and scale, but it also helped with their digital But delivering a better customer experience, and supporting transformation because it brings together data from dispa- these loads, is not easy. rate systems and makes it easily accessible. First, existing applications need more speed and scale. This white paper provides an overview of IMDGs, as well as Over the last decade, query and transaction volumes have IMDG functionality as part of an IMC platform. Its prime focus grown 10 to 1000 times. Data has grown 50 times. End is on how companies implement IMDGs to improve speed consumers now expect sub-second response times, every and scale for existing applications, and how this is the first time, which is easily 1000 times faster than the hours or step in their larger digital transformation. It also introduces days that many traditional processes traditionally took to Apache® Ignite™ and the GridGain® in-memory computing complete. This 10 to 1000 times growth in speed and scale platform, and how companies use Ignite and GridGain first usually overwhelms existing applications first, because new as an IMDG, and then as a broader IMC platform for digital applications need to access the data in existing applications. transformation across new and exisiting applications, APIs, But these legacy applications and their underlying databases and analytics. were not originally designed to handle this growth. 2 © 2019 GridGain Systems, Inc. Improving Speed, Scalability, and the Customer Experience with In-Memory Data Grids WHITE PAPER Second, new applications and APIs also need speed and IMPLEMENTING HTAP AND IMC AS THE scale. Even if existing applications could handle the growth, BRIDGE TO DIGITAL BUSINESS they would only address part of the end-to-end speed and scale challenges. Delivering sub-second response times, Several companies have added both speed and scale to every time, from a mobile or web app requires lowering the existing applications, and delivered a more personalized, total end-to-end latency of: responsive, and real-time customer experience. They have also delivered new capabilities that are much more flexible • The connection from the customer application over the Internet. to change, and added the analytics and decision automation needed to improve the customer experience. • Several layers and network hops of security, APIs, and microservices. Solving these problems requires a major shift in a company’s focus on the customer. But it also requires adopting new • A middleware layer that eventually accesses existing appli- technologies that collect a lot more information and perform cations, which in turn access databases. analytics and decision automation during each transaction It also requires much greater scale than the existing appli- or interaction. Gartner and others call this approach hybrid cations. Legacy applications mostly use “transactional” data, transactional/analytical processing (HTAP). which is about 10 percent of the total data saved by a Many of the companies addressing improving customer company. It is the newer applications and APIs that use the experience implemented HTAP using an in-memory comput- majority data available today, or “Big Data.” ing (IMC) solution. Whatever technology is used for HTAP, it Third, new applications and APIs need to be much more must be able to handle the 10-1000 times increase in inter- flexible to change. IT must also deliver new capabilities in actions and 50 times increase in data. By using IMC, they: weeks (or days) in order to compete with the digital leaders. • Performed analytics and automation in real-time during But these new applications and APIs need to access data their transactions and sub-second interactions with cus- and functionality in existing applications, and those existing tomers. applications usually take months or years to change. • Automated decisions to personalize the experience or Fourth, digital business requires real-time analytics and cross-promote products. automation. Transactional and analytical infrastructures have • Proactively addressed a customer issue before it impacted traditionally been separate, with the operational data getting a customer’s satisfaction, purchasing decision, or loyalty. extracted, transformed, and loaded (ETL) over time into data warehouses and data lakes. This worked because existing IMC added the speed, scale, and distributed computing databases could barely support the transactional workloads, needed for real-time, in-process HTAP. analytics were better served by different technologies any- Companies using IMC for HTAP do not have to rip out and way, and most analytics did not need real-time data. But replace their existing applications and databases. Instead, existing analytics infrastructure cannot deliver the real-time they added speed and scalability by sliding an in-memory analytics needed to improve the customer experience. The data grid (IMDG) between their existing applications and analytics and automation required to replace a process that databases, one application at a time. This cost-effective, took hours, days, or weeks with a “one-click” process must incremental approach enables companies to, over time, build run in the same place as the operational applications in order a new in-memory layer for accessing and processing existing to complete during a “sub-second” interaction. and new data. In-memory computing is the foundation that Fifth, delivering this new experience needs to be built allows them to address other challenges as well. It allows incrementally. Most “big bang” IT initiatives fail. Digital them to combine transactions and services with real-time transformation, and adding speed and scale to applications, analytics over time, and deliver new HTAP applications for must get done incrementally. The architecture and technol- their digital transformations and customer experience initia- ogies used must be flexible enough to be easily added one tives. project at a time. 3 © 2019 GridGain Systems, Inc. Improving Speed, Scalability, and the Customer Experience with In-Memory Data Grids WHITE PAPER ADDING SPEED AND SCALE TO EXISTING For an IMDG to be minimally disruptive to an existing appli- APPLICATIONS WITH AN IMDG cation and not require additional coding, it needs to support the application’s way of accessing data. For most applica- Many companies tried to support ever-increasing query and tions, that is SQL (the second most popular developer lan- transaction growth by solving point performance and scal- guage after JavaScript and the most popular language for ability issues one application at a time. For most applica- data access). tions the bottleneck is the database, and the most common An IMDG is minimally disruptive if it can be added as a trans- approach to the issue was scaling vertically with much more parent proxy, one that does not require a change to the expensive hardware. But this approach was never cost effec- application, in between an application and database. That tive or sustainable in the long run. System loads are growing means it must provide JDBC/ODBC drivers to the applica- faster than annual performance and cost improvements for tion that support standard SQL and the full range of ACID any single server, and this growth shows no signs of slowing transactions developers expect to use in a database. The down. IMDG also needs to connect to and pass the same SQL to Many companies turn to in-memory computing (IMC). Com- the database, and keep the data in sync across the IMDG and panies first started with caches: in-memory data stores databases.