Scale-Out Without Sacrificing SQL

Legacy SQL databases provide low-latency reads but do not have the ability to scale writes across multiple nodes. Distributed NoSQL databases offer performance and write scalability, but give up on SQL semantics such as multi-key access via indexes, ACID transactions and strong consistency. Splice Machine’s auto-sharding and patented ACID transaction architecture enables both low-latency reads, and write-scalability with indexes. So now you don’t have to re-write applications on complex NoSQL architectures to reach petabyte scale or use overly expensive scale-up legacy databases.

One leading payments company modernized their legacy dispute resolution application with Splice Machine. With Splice, the RDBMS application grew to handle petabytes of data but maintained response times in milliseconds.

Run Legacy Applications

Splice Machine’s full ANSI SQL support enables your business to easily migrate your existing business applications that are unable to keep pace with increasing data volume to a modern scale-out platform. Leverage your existing SQL-trained staff without needing an army of scarce distributed system engineers. Also with migration tools, and support for various IBM™ DB2 and Oracle™ constructs, Splice Machine makes it easy to move your legacy applications onto a modern architecture without rewriting them.  

One leading insurance company selected Splice to modernize their legacy client, policy, and claim system with Splice Machine with the goal of not changing their DB2 application.

Deploy on Any Cloud or Stay On-Premise

Now you can deploy a hybrid or even multi-cloud strategy for your data platforms. Splice machine enables you to avoid vendor lock-in and use the computing infrastructure suited to the use case. You can implement on-premise and backup to the cloud. You can operate on Azure and access data on AWS. Or you can go all in with one public cloud and maximize volume discounts and credits. You get to choose.

Leave Operations To Us or Run Them Yourself

You can use Splice Machine as true platform-as-a-service running in the cloud, or you can install and operate clusters with your DevOps team. Splice Machine’s managed service eliminates the administration and management demands of traditional databases and big data platforms. Splice automatically handles infrastructure, optimization, availability, data protection and more so you can focus on using your data, not managing it.

However, if your use case needs to be on-premise or in your private cloud, you can install Splice Machine on Kubernetes and other platform orchestrators and use our management tools to operate the platform with your operational staff. You get to choose.

Unify Analytics Without Latent ETL

Splice Machine is in the class of Hybrid Transactional Analytics Processing systems (HTAP). We unify transactional workloads and analytical workloads on a converged platform.

Splice Machine’s cost-based optimizer dynamically chooses from multiple integrated compute engines based on the workload in milliseconds. This means it can power both your application as well as serve as the data warehouse for that application making ETL only necessary for external data. This means your BI reports and dashboards in Tableau, Microstrategy, PowerBI, etc. always are current and not stale. This also makes complex Lambda Architectures obsolete which require multiple Big Data compute engines to be duct-taped together in a brittle mess.

Take In-the-Moment Intelligent Actions

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.

Never before have businesses been in a better position to drive intelligent in-the-moment actions using all the data that they have at their disposal. Online product recommendations, ad 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 artificial intelligence and machine learning.