Geo-Analytics with Apache Spark and In-Memory Data Grids
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Geo-Analytics with Apache Spark and In-Memory Data Grids. Strata+Hadoop New York 2016
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Geo-Analytics with Apache Spark and In-Memory Data Grids
- 1. Geo-Analytics with Apache Spark and In- Memory Data Grids Copyright © GigaSpaces 2016. All rights reserved. Ali Hodroj VP, Products and Strategy @ahodroj Strata+Hadoop New York 2016
- 2. 2 In-Memory Computing Product Portfolio Direct customers 300+ Fortune / Organizations 40+ / 500+ Cloud Customers 75+ ISVs 25+
- 4. Geo-Spatial Intelligence Beyond just maps and visualizations…. GeoSpatial Intelligence: Confluence of 3 Trends
- 5. 5 GeoSpatial Insight is Essential Financial Services: Detect and prevent fraud by correlating spatial, temporal, and transactional data altogether Retail: Geospatial data in personalization can help retailers optimize their promotional activity based on customer locations Insurance: Risk is often tied to location. Insurers can perform risk simulations against vast amounts of data to come up with the right risk model
- 6. 6 Implementing true GeoSpatial insight Batch + Real-time Data Convergence Closed Loop Analytics (Insights trigger workflows) Multi-tenant, Geo-Federated, Scale-out requires…
- 7. 7 XAP: fast scale-out in-memory data grid Large-scale data processing framework Low-Latency GeoSpatial SQL Apache Spark meets Low-Latency GeoSpatial Processing
- 8. 8 GigaSpaces XAP In-Memory Data Grid Elastic Scale-out In-Memory Storage (Shared-nothing, Linear scalability, Elastic capacity) Low latency and High Throughput (co-located ops, event-driven, fast indexing) High Availability and Resiliency (auto-healing, multi-data center replication, fault tolerance) Rich API and Query Language (SQL, Spring, Java, .NET, C++)
- 9. 9 Pluggable data initial load High Performance Spark with OLTP Capabilities
- 10. 10 Pluggable data initial load
- 11. 11 Spatial Analytics Dynamic Pricing Demo http://insightedge.io/blog
- 12. 12 Case Study: Vehicles/Fleet Spatial Analytics Challenge • Stream data from 1,000s of Taxis • Actively monitor and generate real-time notifications • Location-based tracking, Geo-fencing Solution • Elastically scale stream processing and transactional apps together • Real-time operational intelligence through live in- memory data grid • Extensible and dynamic pricing/routing/fleet rebalancing rules Edge components Data Sources
- 13. Try it out http://insightedge.io @InsightEdgeIO Booth #667
Editor's Notes
- Thanks very much Jason. That was a great background setting up the context for my part of the presentation which is going to follow. I want to talk about the value of in-memory computing and analytics in the context of IoT, and how we see customers utilize these technologies within their environments. Also describe a little about how we approached the IoT market by converging in-memory computing, Spark, and other NoSQL workloads and how we can run those on our systems. By way of background, I’ve been with GS for about 4 years. I Spent a lot of time in the field working with our customers and prospects implementing solutions using GigaSpaces technologies. So this morning, or afternoon, or evening depending where you are. We will talk a little about fast data. One interesting things about IoT, even though it has a
- Just a quick slide about GigaSpaces. We’ve been around for about 15 years. We’re a software company focused on enabling on low latency data processing and connecting insight to actions in realtime. The original product was XAP, which stands extreme application platform. For us this is about real-time, delivering reliable high performance to both humans and machines, if you will, to help derive business results for the company. We’ve been involved in many verticals that demand mission critical, low latency, and fast trasnaction processing. We’re recognized by different analyst firms in terms of leading different technology categories such as in-memory computing and cloud orchestration. We server a portfolio of more than 300 customers, 40 of which are Fortune-500. We also have quite a large deployment footprint through our OEM partners. We’ve been one of the first to digitize wall st. We started in financial services.
- In terms of the organizations that GigaSpaces works with: it’s in some ways the who’s who of the global 2000. This is a list of some of our public facing customers. Use Case #1: Financial services Use Case #2: Retail Use Case #3: Telecommunications Just a taste of the things that companies are doing with the GigaSpaces XAP in-memory data grid.
- …What this means is that there’s a lot of challenges assocaited with architecture complexity as well as performance when it comes to building a foundation for IoT infrastructures.
- …What this means is that there’s a lot of challenges assocaited with architecture complexity as well as performance when it comes to building a foundation for IoT infrastructures.
- One thing that people often ask about, is what is the nature of the underlying data grid, and what kind of API’s do I have to interact with it. How does it differ simply than Spark in-memory or something like Tachyon or others. <an in-memory data grid…etc> We’ll talk more about how these functionalities are used, which is very strategic and important to how we see realtime and fast data analytics pipelines are implemented in the industry.
- One thing that people often ask about, is what is the nature of the underlying data grid, and what kind of API’s do I have to interact with it. How does it differ simply than Spark in-memory or something like Tachyon or others. <an in-memory data grid…etc> We’ll talk more about how these functionalities are used, which is very strategic and important to how we see realtime and fast data analytics pipelines are implemented in the industry.
- So, what I wanted to do now is give a little more detailed example of a reference architecture we see with InsightEdge. Problem. Context – Solution. In sequence of operations…. So, this is the value that people can get by converging In-Memory Data Grid and Spark together.
- I’ll bring the point home on this last slide with talking about a use cases from one of our customers in Europe.