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Pat McGarryRyft Systems, Inc.
Overcoming the Top Five Hurdles to Real-Time Analytics
Information—the fuel of business—is trapped in analysis platforms built on 70-year
old architectures.
Real-time insights as events occur, close to the source of data
Analysis of data from a range of IoT devices—video, mobile, batch stores, etc.—together
Ultra small & efficient analytics infrastructure Easy to deploy, use & maintain systems Low operational costs No security or performance trade-offs
IoT is exacerbating the widening data analytics technology divide.
REQUIREMENTS
Persistent compute/IO/storage bottlenecks Data analyzed in silos Data movement & ETL delays Sprawling inefficient analytics infrastructures Persistent data privacy & security issues
REALITY
T H E C H A L L E N G E
Complex, Closed Systems vsLow Performance Open Source Software
Closed analytics systems are expensive, hard to use and require huge teams to implement
Open source frameworks are easier to use, but their performance is limited by the commodity x86 servers they run on
Organizations have been forced to sacrifice performance or simplicity
T H E C H A L L E N G E
Slow Networking Speeds That Extend Data Transport Times
Current infrastructures do not have the power or efficiency to be put at the network’s edge
Data networking speeds can be slow or unreliable and have a drastic impact on data analytics speeds
T H E C H A L L E N G E
Time Consuming ETL and Indexing Bottlenecks
Traditional x86-based architectures require lengthy Extract, Transform and Load (ETL) and Indexing processes
These processes balloon the data size to an unreasonable degree
Data preparation time often means the difference between actionable insights or poor business decisions
T H E C H A L L E N G E
Complex, and Sometimes Impossible, Analytic Functions
Analytics functions—like fuzzy search—often require more or different computing power than is available in today’s analytics infrastructures
Traditional analytics ecosystems require massive indexes and data preparation functions
The combination of data preparation time and analysis limitations don’t allow for real-time analytics that capture all relevant insights
T H E C H A L L E N G E
Costly, Complex and Inefficient x86-based Clusters
Hardware bottlenecks still stifle data analytics performance
Required data processing, indexing, data sharding and other bottlenecks inherently slow down analytics
Cluster complexity can lead to inferior data center infrastructures that do not provide real-time performance
Heterogeneous (Hybrid) Computing is the solution…
SOURCES: BLOOMBERG BUSINESS, THE PLATFORM
Heterogeneous or hybrid computing refers to systems that use more than one kind of processor or cores. These systems gain performance or energy efficiency not just by adding the same type of processors, but by adding dissimilar processors, usually incorporating specialized processing capabilities to handle particular tasks.
…because optimal performance & efficiency demands the right “engine” for the job.
CPUs FPGA• General purpose
computing
• Sequential in nature
• Non-deterministic performance
• Interrupts• Memory
allocation
• Not general purpose and can be reprogramed via firmware
• Best at data-heavy analysis such as Search, fuzzy search, image and video analysis, deep learning
• Inherently massively parallel to give more output with less power
GPUs• Some general
purpose computing
• Can excel at certain complex algorithms
• Generally more parallel than CPUs, since GPUs have more cores
Performance
CPU FPGAGPU
Open API
CPU FPGAGPU
Coupled with an open, easy-to-use approach with business-centric, compute-agnostic open API.
