Data catalog

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This document provides an overview of a data catalog called Amundsen that was created to improve the productivity of data users. Amundsen indexes data resources and powers search based on usage patterns to help users discover, understand, and analyze data. It aims to reduce the time data scientists spend on data discovery activities from one-third to increase their productivity. The tool provides search of metadata from various data sources and displays table details, column metadata stats, and people profiles to help users find and understand corporate data.

Software

Data Catalog:
Overview
corporate.hrs.com

|
HRS |
Increase the productivity of data users:
● data scientists
● data analyst
● BI engineers
Why do we need it
Title of presentation 2

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Step 1: Search and find the data
Step 2: Understand the data
Step 3: Perform and analysis and visualization
Step 4: Make a decision and/or share insights
Data-Driven Decision Making Process
Title of presentation 3
Data Discovery

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1. Ask coworkers
2. Ask in wider Zoom channel
3. Search over Confluence
4. Search over Repositories
5. Explore using * SQL queries
Challenge: Search and ﬁnd the data
Title of presentation 4

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● Multiple results, which one is correct or
up to date?
● What do different columns mean?
Challenge: Understand the data
Title of presentation 5

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Data scientists spend up to ⅓ time in Data Discovery
Title of presentation 6

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1. Discover new data sources
2. Identify end users to notify them of
changes
3. Understand the popularity and
trustworthiness of data
4. Investigate/monitor the magnitude of
protected data exposure
5. Know what your boss or colleagues are
using
6. Talk to upstream producers
7. +30% productivity for data users
Metadata is the key to next bigdata wave
Title of presentation 7

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What type of questions we want to answer
Title of presentation 8

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Features matrix, to the best of my knowledge
Title of presentation 9

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● First person to explore both North and
South poles
● Norwegian explorer, Roald Amundsen
Amundsen: Person
Title of presentation 10

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• Amundsen is a data discovery and metadata engine for improving the
productivity of data users
• It does that today by indexing data resources (tables, dashboards, streams,
etc.) and powering a page-rank style search based on usage patterns (e.g.
highly queried tables show up earlier than less queried tables)
• Think of it as Google search for data
Amundsen: The tool
Title of presentation 11

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HRS |
Architecture: Key components
Title of presentation 12
Athena MSSql Exasol ... Glue
CI/CD
Source
File
Databuilder Crawler
Neo4j
Elastic
Search
Metadata Service Search Service
Frontend Service
ML
Feature
Service
Security
Service
Other Microservices
Metadata Sources

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Landing page
Title of presentation 13

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Table detail page
Title of presentation 15

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Computed stats about column metadata
Title of presentation 16

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People search
Title of presentation 17

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People page
Title of presentation 18

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ElasticSearch for search and relevance
Title of presentation 19
● Normal search: match records based on relevancy
● Category search: match records first based on data
type, then relevancy
○ column: warehouse_cost
● Wildcard search:
○ event_*

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HRS |
Amundsen uses Apache Airﬂow to orchestrate
Databuilder jobs
Title of presentation 20

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With the aid of any number of data management and processing tools, data flows through multiple on-prem and cloud storage locations before it’s delivered to business users. As a result, IT teams — including IT Ops, DataOps, and DevOps — are often overwhelmed by the complexity of creating a reliable data pipeline that includes the automation and observability they require. The answer to this widespread problem is a centralized data pipeline orchestration solution. Join Stonebranch’s Scott Davis, Global Vice President and Ravi Murugesan, Sr. Solution Engineer to learn how DataOps teams orchestrate their end-to-end data pipelines with a platform approach to managing automation. Key Learnings: - Discover how to orchestrate data pipelines across a hybrid IT environment (on-prem and cloud) - Find out how DataOps teams are empowered with event-based triggers for real-time data flow - See examples of reports, dashboards, and proactive alerts designed to help you reliably keep data flowing through your business — with the observability you require - Discover how to replace clunky legacy approaches to streaming data in a multi-cloud environment - See what’s possible with the Stonebranch Universal Automation Center (UAC)

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Watch full webinar here: https://buff.ly/2Vq9FR0 Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use. In this session, you will learn: *How data catalogs enable enterprise-wide data governance regimes *What key capability requirements should you expect in data catalogs *How data virtualization combines dynamic data catalogs with delivery

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A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.

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Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word. In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments. Bob will address the following in this webinar: - A comparison of active and passive Data Governance - What it means to have an active Data Governance program - How a data catalog tool can be used to activate data stewards - The role a data catalog plays in Data Governance - The metadata in the data catalog will not govern itself

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The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include: - Understanding how to leverage metadata practices in support of business strategy - Discuss foundational metadata concepts - Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy Metadata strategies include: - Metadata is a gerund so don’t try to treat it as a noun - Metadata is the language of Data Governance - Treat glossaries/repositories as capabilities, not technology

The Importance of Metadata

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Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale. At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including: Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal Performing data quality validations using libraries built to work with spark Dynamically generating pipelines that can be abstracted away from users Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time

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Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about: Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles? Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.

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Data is increasingly becoming core to many products. Whether to provide recommendations for users, getting insights on how they use the product, or using machine learning to improve the experience. This creates a critical need for reliable data operations and understanding how data is flowing through our systems. Data pipelines must be auditable, reliable, and run on time. This proves particularly difficult in a constantly changing, fast-paced environment. Collecting this lineage metadata as data pipelines are running provides an understanding of dependencies between many teams consuming and producing data and how constant changes impact them. It is the underlying foundation that enables the many use cases related to data operations. The OpenLineage project is an API standardizing this metadata across the ecosystem, reducing complexity and duplicate work in collecting lineage information. It enables many projects, consumers of lineage in the ecosystem whether they focus on operations, governance or security. Marquez is an open source project part of the LF AI & Data foundation which instruments data pipelines to collect lineage and metadata and enable those use cases. It implements the OpenLineage API and provides context by making visible dependencies across organizations and technologies as they change over time.

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We specialize in Psychic Readings, Psychic Love Spells, Binding Love Spells, Obsession Spells, Voodoo Spells, Lottery Spells, Marriage Spells, Black Magic Spells, Palm Readings & much more. Are you depressed? We perform this come-to-me love spell that works instantly with the aim of Winnipeg back the victim to the person performing the magic. Have you lost your lover? We perform this come-to-me love spell that works instantly with the aim of bringing back the victim to the person performing the magic. Have you lost your lover? Do u need to solve any relationship problem? Contact the powerful spells caster chief kule with love spells that work overnight and love spells that really work. Have you found yourself infatuated with a special someone you think could be the one? Are you looking for a spell to provide them with a nudge in the right direction? Or maybe the spell you cast didn’t achieve the results you were hoping for? Whether you’re new or versed in the ways of spell casting, we’re here to help. Today we’re going to provide you with a detailed guide on the types of love spells to cast. Not only that but there’s something for those who wish to find outside advice from more advanced spell casters. We’re also going to provide you with the top sites available to help you with your dilemma. Let’s begin our journey by educating ourselves on love magic and what a real love caster looks like. Love Magic and Love Casters Love magic made its first appearance back in Ancient Egypt and has been an active practice since. This type of magic is a branch of traditional magic and can be practiced in various ways. Typically the more common use of love magic is through the work of spells, but other methods look like Charms Rituals-LOVE Potions-Dolls and even Amulets If you are interested in becoming a love caster, be prepared for what’s to come. A genuine love caster knows that the art of love casting is no easy feat and shouldn’t be done casually. You should know that not only does it require you to be gifted spiritually, but you must be ready to serve others. Someone who is considered a real love caster has experience in all manner of spells, no matter the difficulty. Training yourself in attraction, commitment, and marriage spells is an excellent place to start. But this by no means will make you a professional. Practice your craft and expand your knowledge; understand that you will possess the ability to help others in time truly. Types of Love Spells What better way to start broadening your experiences with love spells than by learning more about them? These spells work like just about any other spell. Simply apply your intention, use a medium (sigils, mantras, candles, or charm bags), and top it off with establishing the belief that you will receive what you want. So what kind of spells are available and which ones suit your needs the best? Let’s take a look at the many options you have at your disposal. Attraction Spells

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Data catalog

1. Data Catalog: Overview corporate.hrs.com

2. | HRS | Increase the productivity of data users: ● data scientists ● data analyst ● BI engineers Why do we need it Title of presentation 2

3. | HRS | Step 1: Search and find the data Step 2: Understand the data Step 3: Perform and analysis and visualization Step 4: Make a decision and/or share insights Data-Driven Decision Making Process Title of presentation 3 Data Discovery

4. | HRS | 1. Ask coworkers 2. Ask in wider Zoom channel 3. Search over Confluence 4. Search over Repositories 5. Explore using * SQL queries Challenge: Search and ﬁnd the data Title of presentation 4

5. | HRS | ● Multiple results, which one is correct or up to date? ● What do different columns mean? Challenge: Understand the data Title of presentation 5

6. | HRS | Data scientists spend up to ⅓ time in Data Discovery Title of presentation 6

7. | HRS | 1. Discover new data sources 2. Identify end users to notify them of changes 3. Understand the popularity and trustworthiness of data 4. Investigate/monitor the magnitude of protected data exposure 5. Know what your boss or colleagues are using 6. Talk to upstream producers 7. +30% productivity for data users Metadata is the key to next bigdata wave Title of presentation 7

8. | HRS | What type of questions we want to answer Title of presentation 8

9. | HRS | Features matrix, to the best of my knowledge Title of presentation 9

10. | HRS | ● First person to explore both North and South poles ● Norwegian explorer, Roald Amundsen Amundsen: Person Title of presentation 10

11. | HRS | • Amundsen is a data discovery and metadata engine for improving the productivity of data users • It does that today by indexing data resources (tables, dashboards, streams, etc.) and powering a page-rank style search based on usage patterns (e.g. highly queried tables show up earlier than less queried tables) • Think of it as Google search for data Amundsen: The tool Title of presentation 11

12. | HRS | Architecture: Key components Title of presentation 12 Athena MSSql Exasol ... Glue CI/CD Source File Databuilder Crawler Neo4j Elastic Search Metadata Service Search Service Frontend Service ML Feature Service Security Service Other Microservices Metadata Sources

13. | HRS | Landing page Title of presentation 13

14. | HRS | Search Title of presentation 14

15. | HRS | Table detail page Title of presentation 15

16. | HRS | Computed stats about column metadata Title of presentation 16

17. | HRS | People search Title of presentation 17

18. | HRS | People page Title of presentation 18

19. | HRS | ElasticSearch for search and relevance Title of presentation 19 ● Normal search: match records based on relevancy ● Category search: match records first based on data type, then relevancy ○ column: warehouse_cost ● Wildcard search: ○ event_*

20. | HRS | Amundsen uses Apache Airﬂow to orchestrate Databuilder jobs Title of presentation 20

21. THANK YOU

Data catalog

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