IoT is a Really Big Deal
We are on our way to 50 billion Internet connected devices. While early IoT devices sent simple on/off messages, we now have devices like smart phones, autonomous cars, and industrial robots that generate huge amounts of data. Data is exploding and overwhelms traditional databases, leading to failing applications and missed opportunities.
Why Choose Splice Machine for IoT Applications?
Splice Machine is a scale-out application platform for IoT that encompasses data stream processing, data transformation, support for hybrid OLTP and OLAP processing, and Machine Learning.
Data Ingestion
Splice Machine integrates tightly with messaging systems such as Kafka and Kinesis. It can insert data under transaction control, perform parallel batch processing across many database nodes or perform real-time transformations before committing data to the database.
Transformation
Splice Machine’s in-memory Spark Engine can perform real-time analytics on incoming data, create batches for distributed insertion and perform on-the-fly transformations to make sure that committed data is immediately available for analysis and machine learning.
Dynamic Scale-Out
Splice Machine is a Cloud-based platform that scales out (and back) across industry standard compute and storage resources without service interruption. Whatever the volume and velocity of your data, Splice Machine is ready to turn it into information and decisions.
OLTP Processing
In Splice Machine data is immediately available for processing questions such as: “When will that shipment arrive?” “Which devices will require service?” “What is the user profile for this cookie-id?” This makes it the ideal platform to run operational applications on large data streams.
OLAP Processing
Splice Machine can store data in row-based and in columnar formats. Its cost-based optimizer creates query execution plans that make optimum use of either. You can do analysis and track trends across real-time data without interfering with ingestion speeds or transactional applications.
Machine Learning
As data comes in, applications must adapt to what can be learned from these data flows. Splice Machine enables data scientists to develop models using production data and deploy better models in real-time, so that IoT applications become continuous decision making systems.
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