Data Lakehouse Symposium | Day 1 | Part 2

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The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

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A presentation by
W H Inmon
LAKEHOUSE AND WAREHOUSE –
A COMPARISON

applications
ETL Data
warehouse
The data warehouse infrastructure

applications
Data
warehouse
Gender – m/f
Distance - inches
Gender – male/female
Distance - feet
Gender – 1/0
Distance - cms Gender – m/f
Distance - feet
A fundamental transformation of data occurs

Data
warehouse
Data warehouse –
subject oriented
integrated
non volatile
time variant
collection of data for management decisions
The single version of the truth

Data
warehouse
Structured, transaction based data

High business value
Low business value
Some data has high business value,
much data has little business value

An analytical infrastructure is placed over the data
lake

The data lakehouse The data warehouse
How are the data lakehouse and the data warehouse
The same? How are they different?

The data lakehouse The data warehouse
The same -
Both exist to serve analytical processing
Both have vetted data – accurate, reliable
Both can look at data over a long period of time
Both have tame variant data
Both have data that once recorded cannot be changed

The data lakehouse The data warehouse
Different -
There is MUCH MORE data in the lakehouse
The data is fundamentally different –
warehouse data is transaction based data
lakehouse data is machine generated data
The data structures are very different
Relating the two types of data is difficult to do

The data lakehouse The data warehouse
Like cousins -
Related yet still different

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Watch full webinar here: https://bit.ly/3rwWhyv The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization. Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes. In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture. You will learn: - How data mesh architecture not only enables better performance and agility, but also self-service data access - The requirements for “data products” in the data mesh world, and how data virtualization supports them - How data virtualization enables domains in a data mesh to be truly autonomous - Why a data lake is not automatically a data mesh - How to implement a simple, functional data mesh architecture using data virtualization

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Every business today wants to leverage data to drive strategic initiatives with machine learning, data science and analytics — but runs into challenges from siloed teams, proprietary technologies and unreliable data. That’s why enterprises are turning to the lakehouse because it offers a single platform to unify all your data, analytics and AI workloads. Join our How to Build a Lakehouse technical training, where we’ll explore how to use Apache SparkTM, Delta Lake, and other open source technologies to build a better lakehouse. This virtual session will include concepts, architectures and demos. Here’s what you’ll learn in this 2-hour session: How Delta Lake combines the best of data warehouses and data lakes for improved data reliability, performance and security How to use Apache Spark and Delta Lake to perform ETL processing, manage late-arriving data, and repair corrupted data directly on your lakehouse

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Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.

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The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.

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Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.

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A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020. Abstract: The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability. At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh. The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership. This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.

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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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Organizations have been chasing the dream of data democratization, unlocking and accessing data at scale to serve their customers and business, for over a half a century from early days of data warehousing. They have been trying to reach this dream through multiple generations of architectures, such as data warehouse and data lake, through a cambrian explosion of tools and a large amount of investments to build their next data platform. Despite the intention and the investments the results have been middling. In this keynote, Zhamak shares her observations on the failure modes of a centralized paradigm of a data lake, and its predecessor data warehouse. She introduces Data Mesh, a paradigm shift in big data management that draws from modern distributed architecture: considering domains as the first class concern, applying self-sovereignty to distribute the ownership of data, applying platform thinking to create self-serve data infrastructure, and treating data as a product. This talk introduces the principles underpinning data mesh and Zhamak's recent learnings in creating a path to bring data mesh to life in your organization.

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A Work of Zhamak Dehghani Principal consultant ThoughtWorks https://martinfowler.com/articles/data-monolith-to-mesh.html https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.

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Hyperparameter tuning is critical in model development. And its general form: parameter tuning with an objective function is also widely used in industry. On the other hand, Apache Spark can handle massive parallelism, and Apache Spark ML is a solid machine learning solution. But we have not seen a general and intuitive distributed parameter tuning solution based on Apache Spark, why? Not every tuning problem is on Apache Spark ML models. How can Apache Spark handle general models? Not every tuning problem is a parallelizable grid or random search. Bayesian optimization is sequential, how can Apache Spark help in this case? Not every tuning problem is single epoch, deep learning is not. How to fit algos such as hyperband and ASHA into Apache Spark? Not every tuning problem is a machine learning problem, for example simulation + tuning is also common. How to generalize? In this talk, we are going to show how using Fugue-Tune and Apache Spark together can eliminate these painpoints Fugue-Tune like Fugue, is a “super framework” – an absraction layer unifying existing solutions such as Hyperopt and Optuna It firstly models the general tuning problems, independent from machine learning It is designed for both small and large scale problems. It can always fully parallelize the distributable part of a tuning problem It works for both classical and deep learning models. With Fugue, running hyperband and ASHA becomes possible on Apache Spark. In the demo, you will see how to do any type of tuning in a consistent, intuitive, scalable and minimal way. And you will see a live demo of the amazing performance.

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When it comes to Large Scale data processing and Machine Learning, Apache Spark is no doubt one of the top battle-tested frameworks out there for handling batched or streaming workloads. The ease of use, built-in Machine Learning modules, and multi-language support makes it a very attractive choice for data wonks. However bootstrapping and getting off the ground could be difficult for most teams without leveraging a Spark cluster that is already pre-provisioned and provided as a managed service in the Cloud, while this is a very attractive choice to get going, in the long run, it could be a very expensive option if it’s not well managed. As an alternative to this approach, our team has been exploring and working a lot with running Spark and all our Machine Learning workloads and pipelines as containerized Docker packages on Kubernetes. This provides an infrastructure-agnostic abstraction layer for us, and as a result, it improves our operational efficiency and reduces our overall compute cost. Most importantly, we can easily target our Spark workload deployment to run on any major Cloud or On-prem infrastructure (with Kubernetes as the common denominator) by just modifying a few configurations. In this talk, we will walk you through the process our team follows to make it easy for us to run a production deployment of our Machine Learning workloads and pipelines on Kubernetes which seamlessly allows us to port our implementation from a local Kubernetes set up on the laptop during development to either an On-prem or Cloud Kubernetes environment

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1. A presentation by W H Inmon LAKEHOUSE AND WAREHOUSE – A COMPARISON

2. applications ETL Data warehouse The data warehouse infrastructure

3. applications Data warehouse Gender – m/f Distance - inches Gender – male/female Distance - feet Gender – 1/0 Distance - cms Gender – m/f Distance - feet A fundamental transformation of data occurs

4. Data warehouse Data warehouse – subject oriented integrated non volatile time variant collection of data for management decisions The single version of the truth

5. Data warehouse Historical data as well.

6. Data warehouse Structured, transaction based data

7. The data lakehouse

8. Machine generated data

9. A lot of data generated

10. High business value Low business value Some data has high business value, much data has little business value

11. The data is put into a data lake

12. An analytical infrastructure is placed over the data lake

13. The data lakehouse The data warehouse How are the data lakehouse and the data warehouse The same? How are they different?

14. The data lakehouse The data warehouse The same - Both exist to serve analytical processing Both have vetted data – accurate, reliable Both can look at data over a long period of time Both have tame variant data Both have data that once recorded cannot be changed

15. The data lakehouse The data warehouse Different - There is MUCH MORE data in the lakehouse The data is fundamentally different – warehouse data is transaction based data lakehouse data is machine generated data The data structures are very different Relating the two types of data is difficult to do

16. The data lakehouse The data warehouse Like cousins - Related yet still different

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