Data Lakehouses Explained

Summary

This script explores the logistics of a restaurant kitchen as a metaphor for data management, comparing the process of turning raw ingredients into meals to how organizations handle data. It delves into the challenges of data lakes and warehouses, introducing the concept of a 'data lakehouse' that combines the best of both to manage data efficiently, ensuring governance and enabling high-performance analytics and machine learning.

Takeaways

• 🍽️ The logistics of a restaurant involve turning raw ingredients into delicious meals, which is a process that can be analogous to managing data in an organization.
• 🚚 In a commercial kitchen, raw ingredients are delivered, processed, and stored in a way that ensures freshness and organization, similar to how data is handled in a data architecture.
• 📦 Ingredients are sorted, labeled, and routed to the correct storage areas, which is comparable to the organization of data in a data architecture.
• 🗃️ Data lakes serve as a place to dump various types of data for later use, much like a kitchen's storage areas for ingredients.
• 🧊 Data lakes are cost-effective for capturing large volumes of data but can become data swamps with issues of data governance and quality.
• 🔍 Data warehouses are optimized for query performance and maintaining data governance and quality, but they can be costly and slow for certain applications.
• 🌐 Data comes from various sources, including cloud environments, operational applications, and social media, similar to how a kitchen receives ingredients from different suppliers.
• 🛠️ The data lakehouse is a new technology that combines the best of data lakes and data warehouses, offering flexibility, cost-effectiveness, performance, and structure.
• 📈 A lakehouse architecture allows for the storage of data from numerous sources and supports both business intelligence and high-performance machine learning workloads.
• 🚀 The lakehouse can be used to modernize existing data lakes or complement data warehouses, especially for AI and machine learning driven workloads.
• 🍴 The analogy of a restaurant's kitchen process highlights the importance of efficient data management and the potential of the lakehouse approach in data architecture.

Q & A

What is the primary challenge faced by a commercial kitchen in terms of logistics?

The primary challenge is processing and organizing the raw ingredients efficiently, ensuring they are sorted, labeled, and routed to the correct storage areas while minimizing food waste and spoilage.

How does the process of managing raw ingredients in a restaurant compare to data management in an organization?

Both processes involve receiving, sorting, and storing items from various sources, ensuring they are organized and ready for use, whether it's cooking a meal or generating business insights.

What is a data lake in the context of data architecture?

A data lake is a storage repository that allows an organization to capture raw, structured, unstructured, and semi-structured data in a cost-effective manner.

What are the main functions of an enterprise data warehouse (EDW)?

An EDW is designed to load, organize, and optimize data for specific analytical tasks, powering business intelligence workloads such as dashboards and reports, and feeding into other analytical tools.

Why can data lakes sometimes become data swamps?

Data lakes can become data swamps due to the accumulation of duplicate, inaccurate, or incomplete data, which makes it difficult to track, manage, and maintain data quality and governance.

What are some of the limitations of data lakes in terms of data governance and query performance?

Data lakes may face challenges with data governance due to the lack of structure and organization, and they may struggle with query performance because they are not optimized for complex analytical queries.

What are the advantages of using a data warehouse for analytical tasks?

Data warehouses offer exceptional query performance, are optimized for maintaining data governance and quality, and support specific analytical tasks and business intelligence workloads.

What are the limitations of data warehouses in terms of data variety and freshness?

Data warehouses have limited support for semi-structured and unstructured data sources, which are growing in importance, and they may be too slow for applications requiring the freshest data due to the time needed to process and load data.

What is a data lakehouse and how does it combine the features of data lakes and data warehouses?

A data lakehouse is a new technology that combines the flexibility and cost-effectiveness of a data lake with the performance and structure of a data warehouse, allowing for efficient storage and management of diverse data sources while supporting both business intelligence and high-performance machine learning workloads.

How can a data lakehouse help modernize existing data lakes and complement data warehouses?

A data lakehouse can be used to modernize existing data lakes by adding built-in data management and governance layers, and it can complement data warehouses by supporting new types of AI and machine learning-driven workloads that require fresher data.

What is the intended takeaway for viewers when dining at a restaurant, as suggested by the video?

The intended takeaway is to consider the logistics and processes that go into preparing the meal, drawing a parallel to the steps taken by ingredients from the kitchen to the plate, and to think about the similar processes in data management.

Outlines

00:00

🍽️ The Logistics of a Restaurant Kitchen

This paragraph discusses the complex logistics behind running a restaurant, from receiving raw ingredients to preparing meals. It draws a parallel between the process of managing a commercial kitchen and the data management within organizations. The author explains how ingredients are delivered, sorted, labeled, and stored in different areas such as pantries and walk-in fridges and freezers, emphasizing the importance of organization for food safety and to minimize waste. The paragraph then transitions into a discussion about data, likening the flow of ingredients into a restaurant to the flow of data into an organization, highlighting the need for efficient data handling to support business operations effectively.

05:06

💧 Challenges of Data Lakes and the Emergence of Data Lakehouses

The second paragraph delves into the challenges faced with data lakes, such as issues with data governance, data quality, and the potential for data to become stale or a 'data swamp'. It contrasts the cost-effectiveness of data lakes with their limitations in handling complex analytical queries. The paragraph then introduces data warehouses as a solution for high query performance but notes their high costs and limitations with semi-structured and unstructured data. The author presents the concept of a 'data lakehouse', a technology that combines the best features of both data lakes and data warehouses. The data lakehouse is described as a flexible, cost-effective solution that supports both business intelligence and high-performance machine learning workloads, with built-in data management and governance. The paragraph concludes with suggestions on how to utilize a lakehouse and invites viewers to consider the logistics of a restaurant meal, drawing a parallel to the journey of data from source to insight.

Keywords

💡Logistics

Logistics refers to the detailed organization and implementation of a complex operation or the management of the flow of things between the point of origin and the point of consumption. In the context of the video, logistics is used to describe the process of how a restaurant handles the delivery and organization of raw ingredients, which is analogous to how data is managed within an organization's data architecture.

💡Commercial Kitchen

A commercial kitchen is a large-scale kitchen found in restaurants, hotels, and other establishments where food is prepared on a large scale for the public. The video uses the commercial kitchen as a metaphor to explain the complexities of data management, drawing parallels between the preparation of ingredients in a kitchen and the handling of data in a data architecture.

💡Data Architectures

Data architectures are the schemes and structures that define how an organization manages and uses its data resources. The video script discusses how data, like ingredients in a kitchen, must be properly managed and organized to be valuable, emphasizing the importance of data architecture in ensuring data is used effectively within an organization.

💡Data Lakes

Data lakes are storage repositories that hold a vast amount of raw data in its native format until it is needed. The script uses the concept of a data lake to illustrate the initial stage of data storage where data from various sources is collected without much immediate processing, much like raw ingredients are stored in a restaurant before being prepared for cooking.

💡Enterprise Data Warehouses (EDWs)

Enterprise Data Warehouses are large-scale, integrated data repositories that provide a single source of data for business intelligence and analytical applications. The video explains that EDWs are where data is organized and optimized for specific analytical tasks, similar to how ingredients are sorted and stored in pantries and freezers in a restaurant.

💡Data Governance

Data governance involves the overall management of the availability, usability, integrity, and security of the data used in an organization. The script points out the challenges of maintaining data governance in data lakes due to the potential for data to become duplicated, inaccurate, or incomplete, which can undermine the quality and reliability of the data.

💡Data Quality

Data quality refers to the overall integrity and reliability of data, which is crucial for making informed decisions. The video script highlights the importance of data quality in both data lakes and data warehouses, noting that poor data quality can lead to ineffective decision-making, just as spoiled ingredients would result in poor meals in a restaurant.

💡Data Swamps

The term 'data swamps' is used in the script to describe data lakes that have become disorganized and filled with low-quality data. This analogy extends the food theme by likening a data swamp to a kitchen where ingredients have spoiled or are not properly managed, rendering them useless for cooking.

💡Business Intelligence (BI)

Business Intelligence refers to the activities of analyzing data to support decision-making within an organization. The script mentions BI as one of the applications that can benefit from well-organized data in an enterprise data warehouse, where data is used to build dashboards and reports to inform business strategies.

💡Data Lakehouse

A data lakehouse is a new technology that combines the best features of data lakes and data warehouses. The script introduces the concept of the data lakehouse as a solution that offers the flexibility and cost-effectiveness of data lakes along with the performance and structure of data warehouses, allowing for efficient storage and utilization of data.

💡Machine Learning Workloads

Machine learning workloads refer to the processes and tasks involved in training and deploying machine learning models. The video script suggests that the data lakehouse architecture can support these workloads by providing the necessary data management and governance layers, enabling the quick and efficient use of data for machine learning applications.

Highlights

The logistics of a restaurant are compared to data management, emphasizing the transformation of raw ingredients into meals, which parallels data processing.

Commercial kitchens receive raw ingredients on pallets, highlighting the initial stage of data ingestion in organizations.

The necessity of unwrapping and processing pallets of ingredients is likened to sorting and labeling data for storage.

Different storage areas for ingredients, such as pantries and freezers, are analogous to data storage solutions like data lakes and warehouses.

The importance of using ingredients before they expire is compared to the need for timely data processing to prevent waste and maintain data integrity.

The role of data in an organization is discussed, with data coming from various sources similar to ingredients from different suppliers.

Data lakes are introduced as repositories for capturing raw data in various formats, akin to storing diverse ingredients.

Enterprise data warehouses (EDWs) are presented as optimized storage for running specific analytical tasks, similar to organized kitchen storage for efficient cooking.

Challenges with data governance and quality in data lakes are compared to the issues of managing a restaurant's inventory.

The risk of data lakes becoming data swamps due to poor data management is highlighted, drawing a parallel to spoilage of unused ingredients.

Query performance issues in data lakes are discussed, comparing the difficulty of extracting insights to the challenge of cooking with unprocessed ingredients.

Data warehouses are praised for their query performance but criticized for their high costs and limitations with newer data types.

The concept of a data lakehouse is introduced, combining the benefits of data lakes and warehouses for a more balanced data architecture.

The data lakehouse is described as offering flexibility, cost-effectiveness, performance, and structure for managing diverse data sources.

Potential uses for a data lakehouse include modernizing existing data lakes and complementing data warehouses for AI and machine learning workloads.

The transcript concludes with an invitation to reflect on the meal preparation process in a restaurant as a metaphor for data management.

A call to action for viewers to like, subscribe, and comment if they have questions about the presented data management concepts.

Transcripts

So, last week I'm having dinner at this restaurant and I'm looking around,

the place is packed, everyone's getting their orders on time,

and I couldn't help but think about the logistics that go into a restaurant,

turning raw ingredients into these delicious meals.

So, let's think about this for a minute.

So in a commercial kitchen we have raw ingredients being delivered by trucks

to our loading dock on large pallets.

So a truck comes in to the loading dock.

They drop off the pallet and the truck is back out on the road to deliver more ingredients to other restaurants.

So that's the easy part.

Now we actually have to unwrap this pallet and process it, right?

We have to sort everything on it.

We have to label all of our ingredients.

And then we also have to make sure that each item

is routed to the correct storage area.

So these things could be going into a pantry for dry goods,

or it could also be going into large walk-in fridges and freezers

for things like fresh vegetables and meats.

And we also have to organize those storage areas, right?.

So we've got to make sure that ingredients that are expiring first are used first.

We've got to make sure certain ingredients are separated from one another for contamination reasons.

And we also have to make sure that certain ingredients

hit a very certain temperature, also for food safety.

And by the way, we need to do all of this as quickly as possible.

To minimize things like food waste.

To minimize spoilage that we could see from the ingredients just sitting on the truck or on a pallet.

And without this process, the cooks in the kitchen can't really do their job as effectively or safely.

They'd be spending a lot of their time just looking for ingredients and less time actually cooking

and serving out meals to their customers, right?

Okay, so what does this have to do with data?

Well, if we think about it,

this very same process also exists within data architectures of organizations.

So, you've got all sorts of different data coming into your organizations from different sources,

such as in different cloud environments,

different operational applications.

Now we even have social media data, right?

All this is coming in to our organization,

just like a kitchen has ingredients coming from different suppliers.

Okay, so we constantly have data coming in.

We need a quick place to dump all different types of data

in different formats for later use.

So, we have data lakes.

Now these lakes allow us to cheaply and quickly capture raw, structured,

and unstructured, and even semi-structured data.

Okay, so now, just like in the kitchen, we're not really cooking on the loading dock, right?

Now, maybe I can put a tiny grill there if I really wanted to,

but we have to organize and transform this data from its raw state

into something that's usable for the kind of insights and analytics that our business wants to generate.

So we have enterprise data warehouses, or "EDWs",

where data is loaded in, sometimes from a data lake,

but sometimes from other sources like operational applications,

and it's optimized and organized to run very specific analytical tasks.

Now, this could be powering different business intelligence, or "BI", workloads, such as building dashboards and reports,

or it could be feeding into other analytical tools.

Just like our pantries and freezers,

the data in the warehouse is cleaned, organized, governed and should be trusted for integrity.

Okay, so what are some of the challenges that we see in this approach?

Well, as we said, data lakes are really awesome to capture tons of data in a cost effective way,

but we run into challenges with data governance and data quality.

And a lot of times these data leaks can become data swamps.

And this happens when there's a lot of duplicate, inaccurate or incomplete data, making it difficult to track and manage assets.

So if you think about it, what happens when that data becomes stale?

Well, it loses its value in creating insights,

the same way that ingredients go bad over time in a restaurant if we don't use them.

So, data lakes also have challenges with query performance

since they're not built and optimized to handle the complex analytical queries,

it can sometimes be tough to get insights out of lakes directly.

Okay, so let's take a look at the data warehouse now.

Now, these are really great at query performance.

They're exceptional.

But they can come at a high cost, right?

Just like those big freezers are can be very costly to run,

we can't put everything into a data warehouse.

Now, they can be better optimized to maintain data governance and quality.

But they have limited support for semi-structured and unstructured data sources -

by the way, the ones that are growing the most that are coming in to our organization -

and they can also sometimes be too slow for certain types of applications that require the freshest data,

because it takes time to sort, clean and load data into the warehouse.

Okay, so what do we do here?

Well, developers took a step back and said,

"Hey, let's take the best of both data lakes and data warehouses

and combine them into a new technology called the data lakehouse".

So we get the flexibility and we get the cost effectiveness of a data lake.

And we get the performance and structure of a data warehouse.

So we'll talk more specifically about the architecture of a data lakehouse in a future video,

but from a value point of view,

the lakehouse lets us store data from the exploding number of new sources in a low cost way,

and then leverages built-in data management and governance layers

to allow us to power both business intelligence

and high performance machine learning workloads quickly.

Okay, so there are plenty of ways that we can start using a lakehouse.

We can modernize our existing data lakes.

We can complement our data warehouses

to support some of these new types of AI and machine learning driven workloads.

But we'll also talk about that in a future video.

So the next time you're at a restaurant, I hope you think about how the meal on your plate got there

and the steps the ingredients took to go from the kitchen to the meal on your plate.

Thank you.

If you like this video and want to see more like it,

please like and subscribe.

If you have questions, please drop them in the comments below.