Digital Transformation 2.0 | Executives

Easily Scale to Meet New Data Requirements Build AI/ML Applications 100x Faster Modernize Applications without Rewrites Consolidate and Offload Costly Legacy Systems Migrate to the Cloud and Avoid Vendor Lock-In

The Quickest and Most Cost-Effective Path to Real-time AI and a Modern Architecture

Startups in all industries typically have data and AI at the heart of their business plan. It is not a question of whether you need to leverage data and AI in your enterprise, it is a question of how fast you can do so and at what cost. This is the main driver of Digital Transformation 2.0. With Splice Machine, AI is more accessible and cost-effective for all enterprises.

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Scale Legacy Applications

Sometimes the world changes and your old systems just can’t scale to the new requirements. For example, payment companies have experienced significant growth in mobile payments. Insurance companies are leveraging mobile, telematics, and industrial IoT data that they never had access to before. Now you don’t need to refactor or rewrite them on a scale-out architecture. Now you can simply migrate your SQL over to Splice Machine and get automatic sharding or distribution of data to achieve unprecedented scale.

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Build New AI Applications 100x Faster with In-the-Moment Decisioning

It is still too hard for teams to build AI applications. It takes a long time for application engineers, data engineers, and data scientists to work together to construct intelligent applications. They all adopt different compute engines and have to figure out how to pass data to one another. This is costly, brittle, and rife with latency. Data scientists rarely get current data to experiment with and deployed ML models rarely have current data profiles to make predictions.

Splice Machine is the only RDBMS that has machine learning native to the database. This enables enterprises to take in-the-moment intelligent actions by providing the most recent data to their ML models and a real-time data science workbench for data scientists to experiment with incredible productivity. Online product recommendations, marketing placements, and decisions to accept or deny a credit card application are just a fraction of the in-the-moment decisions that businesses now routinely make using AI and machine learning.

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Modernize Applications without Rewriting Them

Developers typically stitch together multiple open source and cloud engines to build new ML applications, but it is nearly impossible to modernize applications with these engines without complete re-writes. Now, it is possible with Splice Machine to modernize custom-built applications by making them agile, data-driven, intelligent and cloud-portable.

These custom applications have been overlooked in today’s digital transformation, yet they are the crown jewels of the company’s competitive advantage. And when the magnifying glass is applied to them, developers often leap to refactoring or even rewriting these applications on NoSQL technology. This is no longer necessary. Preserve these valuable assets and extend their lifetime.

Splice Machine’s application modernization journey consists of three steps: migrate to scale-out SQL, consolidate business analytics, and inject artificial intelligence and machine learning.

The benefits of this journey are:

Extended lifetime of an application
Cost reduction due to offloading expensive databases and mainframes
Applications are made smarter with easy extension of new machine learning models

Analytical Offloads

If your SQL data warehouse and analytical data marts are too expensive to expand under unexpected loads, you can easily offload these workloads to Splice Machine. Often users depend on your analytical data for operational use cases. Now you can achieve great analytical performance combined with low-latency access to those applications. You can offload Teradata, Exadata, and Netezza workloads quickly and effectively to cut costs now.

Migrate to the Cloud

Moving IT infrastructure to the cloud is an important part of digital transformation because of the cloud’s inherent agility and scalability, but it doesn’t mean it’s easy. Splice Machine’s containerized managed service on the cloud eliminates the complexities of operating a distributed system for companies, while also ensuring the portability of applications across public clouds with no-lock-in as well as on-premises infrastructure.

Flexible Deployment

Splice Machine is a database that fits your organization, with container-based Kubernetes systems that work on-premise or on the major cloud providers. You can also decide how much Splice Machine you need. Consolidate on a single platform to not only perform analytics and build machine learning models, but also power your purpose-built applications. This simplifies your architecture, reduces costs, and speeds time to value. Or you can easily deploy Splice Machine alongside existing databases, data warehouses and machine learning platforms to add performance, scalability or intelligence with less change.

Leverage Skills and Staff

The first generation of big data, AI, and ML projects required too many specialized skills that bottlenecked the organization because they tended to be kept in silos. Now IT, OT, and engineering can leverage existing skills in SQL and democratize distributed data and AI. Application developers and data engineers can use the same SQL database skills they have mastered for years.

Even the new breed of data scientists in the organization can leverage their skills because Splice Machine offers the same Python-based frameworks they love, but slashes the effort of building data science infrastructure.

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Five Warning Signs That Your Custom Applications Are Being Left Behind

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Business Outcomes of Splice Machine