Real-Time Analytics Visualized w/ Kafka + Streamliner + MemSQL + ZoomData, Anton Gorshkov

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The document summarizes a presentation about using Kafka, Streamliner, MemSQL and ZoomData for real-time analytics visualization. It shows an initial setup with one producer and queue feeding into Kafka, then adding a sink to an in-memory SQL database and real-time visualization consumer. It asks questions about ensuring the system is resilient, handles bad data and schema evolution, maintains consistency across visualization layers, and ability to scale throughput, concurrency and size.

Engineering

Anton Gorshkov
Real-Time Analytics Visualized
Kafka  Streamliner  MemSQL  ZoomData
Please note that during the course of this presentation ZoomData products will be used and shown on the
screen. Goldman Sachs has an ownership interest in ZoomData, Inc. and may have other business relationships
with ZoomData, Inc. Nothing herein shall constitute an offer to sell or a solicitation of an offer to buy an interest in
any entity or product.
Learn more at GS.com/Engineering

>docker run kafka
>docker run memsql
>docker run zoomdata
Initial Set-Up
2 4-CPU / 16GB / 80GB SSD / Intel Xeon E5-2670 @ 2.5GHzX

Start with one producer & a queue
Producer 1 Kafka

Add-a-Sink
Producer 1 Kafka In-Memory
SQL RDB
Consumer

Add a Real-Time Visualization
Producer 1 Kafka In-Memory
SQL RDB
RT VisConsumer

Resilience
- Don’t lose data
- Be Up
- Deliver (at least once and in-order)

How “I” is the BI?
- Is it a “view” or a “do work” layer?
- Data-at-Rest vs Data-at-Motion
- Pull vs Push
- Consistency to other vis layers

Will It Scale?
Throughput, Concurrency, Size
(and at what cost…)

Will it Scale?
Producer 1
Kafka In-Memory
SQL RDB
RT VisProducer 2
Producer n
Consumer

Audience Participation Time…
Kafka In-Memory
SQL RDB
RT Vis
(620) 487-2222
Consumer

Audience Participation Time…
send a text [Fruit] [Quantity]
(620) 487-2222
example: mango 540

Adaptability
Producer 1
Kafka
In-Memory
SQL RDB
RT Vis
Producer 2
Producer n
Elastic
Kibana
Consumer
Kafka
Connect

Representative Deployment
Direct Sink
Spark
Streaming
IMRDBM
S
Data Lake
Kafka
Source
DBs
Order Mgmt
Batch ETL
Cache
VertX

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A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.

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This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks (CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations. When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10 and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing accuracy improvements over previous techniques. The results indicate that the combination of the volumetric input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating adversary training.

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Real-Time Analytics Visualized w/ Kafka + Streamliner + MemSQL + ZoomData, Anton Gorshkov

1. Anton Gorshkov Real-Time Analytics Visualized Kafka  Streamliner  MemSQL  ZoomData Please note that during the course of this presentation ZoomData products will be used and shown on the screen. Goldman Sachs has an ownership interest in ZoomData, Inc. and may have other business relationships with ZoomData, Inc. Nothing herein shall constitute an offer to sell or a solicitation of an offer to buy an interest in any entity or product. Learn more at GS.com/Engineering

2. >docker run kafka >docker run memsql >docker run zoomdata Initial Set-Up 2 4-CPU / 16GB / 80GB SSD / Intel Xeon E5-2670 @ 2.5GHzX

3. Context

4. Start with one producer & a queue Producer 1 Kafka

5. Add-a-Sink Producer 1 Kafka In-Memory SQL RDB Consumer

6. Add a Real-Time Visualization Producer 1 Kafka In-Memory SQL RDB RT VisConsumer

7. Enterprise Grade?

8. Resilience - Don’t lose data - Be Up - Deliver (at least once and in-order)

9. Bad Data

10. Schema Evolution

11. How “I” is the BI? - Is it a “view” or a “do work” layer? - Data-at-Rest vs Data-at-Motion - Pull vs Push - Consistency to other vis layers

12. Will It Scale? Throughput, Concurrency, Size (and at what cost…)

13. Will it Scale? Producer 1 Kafka In-Memory SQL RDB RT VisProducer 2 Producer n Consumer

14. Audience Participation Time… Kafka In-Memory SQL RDB RT Vis (620) 487-2222 Consumer

15. Audience Participation Time… send a text [Fruit] [Quantity] (620) 487-2222 example: mango 540

16. Adaptability Producer 1 Kafka In-Memory SQL RDB RT Vis Producer 2 Producer n Elastic Kibana Consumer Kafka Connect

17. Representative Deployment Direct Sink Spark Streaming IMRDBM S Data Lake Kafka Source DBs Order Mgmt Batch ETL Cache VertX

18. Learn more at GS.com/Engineering

Editor's Notes

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Real-Time Analytics Visualized w/ Kafka + Streamliner + MemSQL + ZoomData, Anton Gorshkov

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Real-Time Analytics Visualized w/ Kafka + Streamliner + MemSQL + ZoomData, Anton Gorshkov

Editor's Notes