Data Con LA 2019 - The challenges of data science for veteran media organizations by Stan Dyro

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An overview of the tools, engineering infrastructure, and decisions that we've been making in concert with our newsroom and executive leadership.- What tools might you use and why? Python, Scala, Spark, AWS, Google GCP, BigQuery, Redshift, Tableau, SQL?- What engineering tradeoffs do you have to make when building data science platforms and choosing big data tools?- How can you empower analysts, engineers, data scientists, and privacy advocates to build a data driven culture ecosystem at at any size company?- How do you recruit, train, and retain world class data talent against competitors like Netflix, Google, and Snap?

Technology

Evangelizing data at media companies
Stan Dyro

Introduction
Who am I?
● Stan Dyro. - Lead Data Engineer at the Los Angeles Times
● Former Lead Engineer at VideoAmp
● Technologist, programmer
● Creative problem solver
Why you should care about data?
● Software is eating the world
● and.. “Data is the fuel”
● We’re hiring!

Technology Direction
● As technology leaders or individual contributors, we have a lot of decisions:
Leaders Engineers Product
Who to hire Who to connect with How you help your company
Structure of teams Which companies to work
for
How to make your company
better
What culture to set Which tools to learn How to help your teams

What’s important and what’s not
● Teams are important. Teams are important.
● People stick around for good bosses. Studies bear this out
● People value the little things.

What’s important and what’s not
The opposite of microaggressions are microprotections:
● Highly paid tech professionals will value contributions over money.
Examples
● Upward mobility
● Ability to learn
● Ability to be proud of what they do
For example:
● Our culture at the LA Times is to “inform, engage and empower.”
● Unless you’re FAANG or flush with VC cash, $400,000 dollar salaries are not an option,
so apply what makes you different

Measure all the things
So let’s talk about data.
Now that you’ve built or worked on helping your great team.
What can you measure about your business?
● Your Customer
● Your Web Traffic
● Your Finances

Structure of a data stack
● Presentation - usually visual tools BI tools, static visualizations
● Databases - Relational data, big data, caching
● Workflow - Workflow tools, programming languages, code repositories
● Storage - Scalable storage, cloud storage options

Why it’s easier than ever
It’s simple.
Open Source.
We stand on the backs of giants in our field. Hadoop. Spark.
Hive. Google Cloud, AWS. The infrastructure is at our
fingertips.
Easy to launch a software business for cheap. So focus on
what matters: your customers.

Data Tools
Databases
● Relational databases
● Data warehouses
● Data lake
Programming Languages
● SQL
● Python
● Ruby
● Javascript / node.js
● C#
● Java
● Scala

Don’t get distracted by the shiny things

Tools - Data Analytics
Business Intelligence
● Tableau Online
● Looker
● Power BI
Visualization
● Tableau
● R Studio
● Python libraries
● D3 / Javascript

Tools - Software
Desktop SQL Clients
● dbForge
● DataGrip
● DataRow (web based client)
● S3 / Cloud Storage Browser
● Transmit (Mac)
● S3 Browser

Tools - Data Engineering
ETL / Job Runners
● Informatica
● Segment
● Airflow
● Luigi
● Custom Solution
Automation / API Integration
● Zapier
● Mulesoft
● Apigee

Tools - Data Science
Notebooks / Collaboration
● Databricks
● Spark on EMR
Cloud Data Warehouses
● Redshift Spectrum
● Snowflake
Scalable Key/Value Databases
● Cassandra
● DynamoDB

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Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

apidays

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

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Data Con LA 2019 - The challenges of data science for veteran media organizations by Stan Dyro

1. Evangelizing data at media companies Stan Dyro

2. Introduction Who am I? ● Stan Dyro. - Lead Data Engineer at the Los Angeles Times ● Former Lead Engineer at VideoAmp ● Technologist, programmer ● Creative problem solver Why you should care about data? ● Software is eating the world ● and.. “Data is the fuel” ● We’re hiring!

3. Technology Direction ● As technology leaders or individual contributors, we have a lot of decisions: Leaders Engineers Product Who to hire Who to connect with How you help your company Structure of teams Which companies to work for How to make your company better What culture to set Which tools to learn How to help your teams

4. What’s important and what’s not ● Teams are important. Teams are important. ● People stick around for good bosses. Studies bear this out ● People value the little things.

5. What’s important and what’s not The opposite of microaggressions are microprotections: ● Highly paid tech professionals will value contributions over money. Examples ● Upward mobility ● Ability to learn ● Ability to be proud of what they do For example: ● Our culture at the LA Times is to “inform, engage and empower.” ● Unless you’re FAANG or flush with VC cash, $400,000 dollar salaries are not an option, so apply what makes you different

6. Measure all the things So let’s talk about data. Now that you’ve built or worked on helping your great team. What can you measure about your business? ● Your Customer ● Your Web Traffic ● Your Finances

7. Structure of a data stack ● Presentation - usually visual tools BI tools, static visualizations ● Databases - Relational data, big data, caching ● Workflow - Workflow tools, programming languages, code repositories ● Storage - Scalable storage, cloud storage options

8. Why it’s easier than ever It’s simple. Open Source. We stand on the backs of giants in our field. Hadoop. Spark. Hive. Google Cloud, AWS. The infrastructure is at our fingertips. Easy to launch a software business for cheap. So focus on what matters: your customers.

9. Data Tools Databases ● Relational databases ● Data warehouses ● Data lake Programming Languages ● SQL ● Python ● Ruby ● Javascript / node.js ● C# ● Java ● Scala

10. It’s easy to get overwhelmed

11.

12. Don’t get distracted by the shiny things

13. Tools - Data Analytics Business Intelligence ● Tableau Online ● Looker ● Power BI Visualization ● Tableau ● R Studio ● Python libraries ● D3 / Javascript

14. Tools - Software Desktop SQL Clients ● dbForge ● DataGrip ● DataRow (web based client) ● S3 / Cloud Storage Browser ● Transmit (Mac) ● S3 Browser

15. Tools - Data Engineering ETL / Job Runners ● Informatica ● Segment ● Airflow ● Luigi ● Custom Solution Automation / API Integration ● Zapier ● Mulesoft ● Apigee

16. Tools - Data Science Notebooks / Collaboration ● Databricks ● Spark on EMR Cloud Data Warehouses ● Redshift Spectrum ● Snowflake Scalable Key/Value Databases ● Cassandra ● DynamoDB

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