Did you like it? Check out our blog to stay up to date: https://getindata.com/blog Currently there are more and more created videos distributed via multiple social media channels. It becomes more and more important to monitor all of them by companies to verify their customers' feedback, reviews, opinions. During the talk, we talk about extracting text from videos, analyzing language and prepare robust, scalable infrastructure for it. The idea behind platform is about having the mix between managed and self-managed service for Big Data processing. The keynote shows the case study of the MVP of the platform for marketing companies. Author: Albert Lewandowski Linkedin: https://www.linkedin.com/in/albert-lewandowski/ ___ Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets. Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries. https://getindata.com​

1. NLP for videos:
Understanding
customers' feelings in
videos
Author: Albert Lewandowski

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About me
● Big Data DevOps Engineer - GetInData
● Focused on infrastructure, cloud, Big Data, AI, scalable
web applications
● Certified Google Cloud Architect
● Certified Kubernetes Administrator

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Content
● Problem to solve
● Big Data Frameworks or not?
● Cloud Magic
● How to mix technologies?
● Observability
● Lessons learnt

4. Problem to solve

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The Problem
Big volume of the videos is complex to be monitored while
more and more young users prefer to mention brands in the
video-based social media.
60%
Of companies
don’t convert
leads into revenue
95%
of a message when
they watch it in a
video, compared to
10% when reading
it in the text
Source: Agility PR, Rick Whittinghton, Hubspot, Insivia
54%
of consumers want to
see more video
content from a brand
or business they
support

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Solution
Scalable
Cloud platform written in Golang, Python and React with
Azure Machine Learning services, and with Apache Spark.
Artificial
Intelligence
Efficient

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~5 - 6 weeks for the project
● Which tools are the fastest in delivering results?
● What is the crucial to meet requirements for PoC?
● How can we analyze language?
● What data do we need to create valuable insights?
● Can we provide scalable platform?

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Perception
Business
logic
CI/CD
Idempotency
Reprocessing
Explainability
Monitoring
Testing
Serving
Infrastructure
Data Ingestion
Security

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Reality
Business logic
CI/CD
Idempotency
Reprocessing
Explainability
Monitoring
Testing
Serving
Infrastructure
Data Ingestion
Security

10. Big Data Frameworks
or not?

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Quick start
1. Linux command line is enough as the entrypoint for the
project.
2. Python script and managed services.
3. Do not reinvent the wheel.

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Big Data tools?
1. Apache Spark can be flexible tool, especially when we
know it and we want to test it with bigger scale.
2. Writing own app in Golang can be a wise choice when we
want to proceed with simple actions like gathering data
from external sources.
3. Limitations of the components
a. Like external SDK

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14. Cloud Magic

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Complex Analysis
Spark seems to be the right solution for it but the speed of
development was more important than creating scalable
solution.
Processing Polish language is really tough and it requires
much more code development.
Spark NLP v3 from John Snow Labs is worth checking.

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Target output
● Frequency of the phrase (like the problem of the product).
● Feelings related to it and if there is only mentioned a
problem or a problem is the main character.
● Each video is tagged with the categories corresponding to:
type of content, feelings, key words.
● Visualizing changes depending on timeperiod.

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Managed Services
Public cloud provides wide range of services but their quality
may differ.
Moreover, pricing can be really high.

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List of steps
1. Get required links to videos.
2. Process video to get only audio.
3. Save audio to storage.
4. Get audio and process it with Azure Cognitive Services to
receive text.
5. Save output to ElasticSearch.
6. Process output to get emotions and feelings based on the
text with Spark.

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Video To Audio To Text
● Azure Cognitive Services
○ It works pretty well with many languages
○ Speech To Text
● Custom implementation
○ It requires a lot of time
○ Required for production use cases

20. How to mix
technologies?

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Microservices - Perfect Match
- We can easily divide the platform
- Data Ingestion - there can be a big number of small parts
of data
- Data processing - no need for real time, batch processing
in Spark works well
- Queue is a must

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What about local setup?
● Docker Compose
○ Apps can be quickly containerized
● Cloud services
○ To mock or not to mock them?
● Remote developer instance
○ Ephemeral Kubernetes clusters might be a good idea also for your case

23. Observability

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Observability

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Observability
Monitoring describes the process of gathering metrics about IT
environment, running applications and observing the system
performance
Observability is about measuring how well internal states of the
system can be inferred from knowledge of its external outputs
(according to the control theory).

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Observability
Example:
- data processing job written in Spark, that rewrites data from
location A to B.
Gathering its metrics and setting up alerts or creating
dashboard with simple runtime visualization are a quite
simple tasks. However to achieve observability we should
collect metrics about the amount of processed data, JVM
statistics and some metrics about infrastructure under the
hood.

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Quick and simple setup
Prometheus
Metrics
Loki with Promtail
Log Analytics

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What about alerts?
Alerts signify that
a human needs to take action
immediately
in response to something that is
either happening or about to
happen, in order to improve the
situation.

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What to monitor?
Errors
Quality and quantity
Data scraping
Self-managed
Compute Resources
Managed
Compute Resources
Performance of NLP
pipelines
Logs monitoring

30. Lessons learnt

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Keep It Simple
● In case of PoC, go with the simplest possible solution.
● Cloud services are always worthy being checked.
● Mixing technologies is a good idea if we already have
know-how within the team

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Corner cases, corner cases
● Remember about corner cases
○ Processing greater number of events
○ Possibility to scale-up and scale-down environments
○ Limitations or downtime of any external services
○ Data Reprocessing
● CICD is always your friend
○ Unit and integration tests are must-have

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Q&A

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Contact details
albert.lewandowski@getindata.com
LinkedIn:
https://www.linkedin.com/in/albert-lewandowski

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