What is Apache Iceberg?

Summary

This video script explores the significance of 'big data' and introduces Apache Iceberg as a modern solution for data management challenges. It compares data management to a library system, highlighting the need for storage, processing power, and metadata. The script traces the evolution of big data from the early 2000s, through the advent of Apache Hadoop and Hive, to the current era dominated by cloud storage and real-time processing. Apache Iceberg is presented as a flexible, efficient, and feature-rich metadata layer that supports various storage systems and processing engines, enabling advanced data governance without significant overhead.

Takeaways

• 📚 Big data is crucial for training, tuning, and evaluating AI models, which are integral to the future of computing.
• 🗂️ Data management systems can be likened to libraries, requiring storage, processing power, and metadata for organization.
• 📈 The scale of data processing has grown significantly with the advent of the Internet and the proliferation of mobile and IoT devices.
• 🛠️ Apache Hadoop was introduced in 2005 to handle large-scale data processing across multiple machines with its HDFS and MapReduce components.
• 🔍 MapReduce's complexity led to the development of Apache Hive in 2008, which translates SQL-like queries into MapReduce jobs and includes the Hive Metastore for optimization.
• 🌐 The shift to cloud-based storage like S3 in the 2010s presented challenges for Hive, which was not designed to interface with such storage systems.
• 🚀 The need for real-time data processing with engines like Presto highlighted Hive's limitations in speed and flexibility.
• 💡 Apache Iceberg, open-sourced in 2017, offers a solution by acting as a metadata layer that sits between storage and compute, providing fine-grained organization and flexibility.
• 🔄 Iceberg supports data versioning, schema evolution, and other data governance features, thanks to its detailed metadata management.
• 🌟 Iceberg's efficiency, flexibility, and feature set make it a popular choice for modern data management, especially with the increasing importance of AI and big data.
• 🤝 The open-source nature of Iceberg encourages community involvement, which is key to its ongoing improvement and adaptation.

Q & A

What is the significance of 'big data' in the context of AI models?

Big data is crucial for training, tuning, and evaluating AI models, which are considered the future of computing. It provides the vast amounts of information needed to develop sophisticated and accurate AI systems.

Why is data management challenging with large datasets?

Managing big data is challenging due to the need for substantial storage, processing power, and metadata organization. It requires efficient systems to handle the scale and complexity of data storage and retrieval.

What is the analogy used in the script to describe a data management system?

The script uses the analogy of a library to describe a data management system, where storage is like bookshelves, processing power is the librarian, and metadata is the organizational system like the Dewey Decimal System.

What was the primary solution to handling big data in the early 2000s?

In the early 2000s, Apache Hadoop was open-sourced as a solution to handle big data. It provided a multi-machine architecture with the Hadoop Distributed File System (HDFS) and a parallel processing model called MapReduce.

What was the main drawback of using MapReduce for processing big data?

MapReduce jobs, being Java programs, were more complex to write compared to SQL statements, creating a barrier for data analysts who were more familiar with SQL.

How did Apache Hive address the limitations of MapReduce?

Apache Hive addressed the limitations by translating SQL-like queries into MapReduce jobs, making it easier for data analysts to work with big data. It also introduced the Hive Metastore for metadata management.

What challenges arose in the 2010s with the increase of mobile and IoT devices?

The increase in mobile and IoT devices led to an exponential growth in data production. This required more scalable and cost-effective storage solutions like cloud-based S3 storage, which Hive could not natively support.

Why was Apache Iceberg introduced, and what problems did it solve?

Apache Iceberg was introduced in 2017 to solve the problems of scalability, storage compatibility, and the need for real-time processing. It provided a flexible, efficient metadata layer that works with various storage systems and processing engines.

How does Apache Iceberg differ from Hive in terms of metadata management?

Iceberg maintains a more fine-grained and detailed metadata picture compared to Hive. This allows for faster and more efficient data processing and querying.

What are some of the new features introduced by Apache Iceberg for data governance?

Apache Iceberg introduced features such as data versioning, asset transactions, schema evolution, and partition evolution, which enhance data governance and integrity.

What is the advantage of Iceberg's approach to metadata management in terms of overhead?

Iceberg's approach to metadata management is efficient and flexible with minimal overhead. It cleverly organizes metadata to provide significant benefits in terms of performance and functionality without requiring extensive additional infrastructure.

Outlines

00:00

📚 Introduction to Big Data and Apache Iceberg

The first paragraph introduces the concept of 'big data' and its significance in the realm of artificial intelligence and computing. It emphasizes the challenges of managing vast amounts of data and introduces Apache Iceberg as a solution. The paragraph also provides an analogy of a data management system to a library, explaining the need for storage, processing power, and metadata. It outlines the evolution of big data from the early 2000s, the advent of the Internet, and the development of Apache Hadoop in 2005 to address the limitations of single-machine data processing. The paragraph concludes with the introduction of Apache Hive in 2008, which aimed to simplify data processing by translating SQL-like queries into MapReduce jobs and introducing the Hive Metastore.

05:04

🔍 The Evolution of Data Management Systems

The second paragraph delves into the evolution of data management systems, particularly focusing on the challenges faced in the 2010s due to the proliferation of mobile and IoT devices, which generated an unprecedented amount of data. The paragraph discusses the shift towards cloud-based storage solutions like S3, which offered scalability and affordability compared to traditional DFS. However, it highlights the limitations of Hive in this new environment, including its inability to interface with S3 and its slow performance for real-time processing. The paragraph also touches on the desire of organizations to leverage their existing data infrastructure and the introduction of Apache Iceberg in 2017 as a solution that addresses these issues. Iceberg is described as a metadata layer that offers fine-grained organization, efficiency, and flexibility, allowing for various processing engines and storage systems to work together seamlessly.

10:04

🛠️ Apache Iceberg's Features and Benefits

The third paragraph highlights the unique features and benefits of Apache Iceberg, focusing on its role in data governance. It explains how Iceberg enables data versioning, asset transactions, schema evolution, and partition evolution, all facilitated by its detailed metadata management. The paragraph uses the library analogy again to illustrate how Iceberg's snapshot feature allows for fine-grained control over data integrity and consistency, akin to maintaining a historical record of the library's contents. The summary concludes by emphasizing Iceberg's efficiency, flexibility, and feature-rich nature, which contribute to its popularity in modern data management, especially in the context of the AI boom in the mid-2020s. The paragraph ends with an encouragement for viewers to engage with the open-source community to further enhance Iceberg's capabilities.

Keywords

💡Big Data

Big Data refers to the large volume of structured, semi-structured, and unstructured data that has the potential to be mined for insights. In the video, it is highlighted as a crucial element for training, tuning, and evaluating AI models, which are pivotal to the future of computing. The term is used to describe the vast amounts of data that organizations handle today, which is significantly more than what a single machine can process, hence the need for solutions like Apache Hadoop and Apache Iceberg.

💡Apache Iceberg

Apache Iceberg is an open-source project that addresses the challenges of managing large-scale data. It is presented in the video as a modern data management solution that offers efficiency, flexibility, and a range of new features for data governance. The script uses the library analogy to explain Iceberg's role in organizing and indexing data, allowing for faster and more precise data access and manipulation.

💡Data Management System

A Data Management System is a framework for organizing, storing, retrieving, and managing data. The video script likens it to a library with vast digital storage, requiring substantial storage, processing power, and metadata to function effectively. It is essential for handling big data and is the core subject of the video, with Apache Iceberg being introduced as a superior system for this purpose.

💡Metadata

Metadata in the context of the video refers to data that provides information about other data. It is compared to the Dewey Decimal System in a library, which helps in organizing and locating content. In data management, metadata is crucial for understanding how data is structured and where it is stored, which is a key feature of Apache Iceberg's approach to data organization.

💡Apache Hadoop

Apache Hadoop is an open-source software framework introduced in 2005 to manage and process big data across distributed computing clusters. The video mentions Hadoop as a solution that provided a multi-machine architecture with the Hadoop Distributed File System (HDFS) and MapReduce for processing data, marking a significant step in the evolution of big data management.

💡MapReduce

MapReduce is a programming model and an associated implementation for processing and generating large datasets. In the video, it is part of Apache Hadoop and is described as a parallel processing model that works with the Hadoop Distributed File System. However, the script also points out that writing MapReduce jobs can be complex compared to SQL queries, indicating a need for more user-friendly interfaces like Apache Hive.

💡Apache Hive

Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. It is highlighted in the script for its ability to translate SQL-like queries into MapReduce jobs, thus simplifying the process for data analysts. Hive also introduced the Hive Metastore, which stores metadata and optimizes queries before they are processed.

💡Hive Metastore

The Hive Metastore is a central repository of metadata for Hive-managed data. It is mentioned in the video as a meta-database that stores pointers to groups of files in the underlying file system. This feature allows for optimized query processing by knowing the data's location before the processing begins, similar to a librarian knowing where specific genres of books are stored.

💡S3 Storage

S3 Storage refers to the Amazon Simple Storage Service, a cloud-based object storage service. The script discusses the shift towards S3 storage due to its affordability and scalability compared to HDFS. However, it also points out that traditional Hive cannot interact with S3, indicating a limitation in data management solutions prior to Apache Iceberg.

💡Data Governance

Data Governance in the video pertains to the overall management of the availability, usability, integrity, and security of the data used in an organization. Apache Iceberg is praised for its data governance features, such as data versioning, asset transactions, schema evolution, and partition evolution. These features are facilitated by Iceberg's detailed metadata, allowing for fine-grained control over data integrity and consistency.

💡Schema Evolution

Schema Evolution is the ability of a system to adapt to changes in the data structure without significant downtime or data loss. In the context of the video, Apache Iceberg supports schema evolution, meaning it can handle changes in the data schema over time, which is a significant feature for managing big data and maintaining system flexibility and robustness.

Highlights

The importance of big data lies in its necessity for training, tuning, and evaluating AI models, which are pivotal to the future of computing.

Managing vast amounts of data is challenging, but Apache Iceberg simplifies the process, making it easier to handle big data.

A data management system can be likened to a library, requiring storage, processing power, and metadata to organize content efficiently.

Data processing today operates at a much larger scale than traditional libraries, hence the term 'big data'.

The evolution of big data began in the early 2000s with the advent of the Internet, leading to an unprecedented amount of data.

In 2005, Apache Hadoop was open-sourced to address the challenge of processing more data than a single machine could handle.

Hadoop consists of the Hadoop Distributed File System and MapReduce, allowing for scalable data processing across multiple machines.

MapReduce jobs, being Java programs, are more complex to write than SQL statements, creating a bottleneck in data processing.

Apache Hive, introduced in 2008, translates SQL-like queries into MapReduce jobs, simplifying the process for data analysts.

Hive also introduced the Hive Metastore, a metadata database that optimizes queries before they are processed by MapReduce.

The scale of data continued to grow in the 2010s due to the proliferation of mobile and IoT devices, necessitating new solutions.

Organizations began turning to cloud-based S3 storage for its affordability and scalability compared to Hadoop's Distributed File System.

Hive's inability to interface with S3 storage and the need for real-time processing highlighted the need for new data management approaches.

Apache Iceberg, open-sourced in 2017, offers a solution by introducing a metadata layer that sits between storage and compute.

Iceberg's fine-grained metadata allows for more efficient and flexible data management compared to Hive.

By decoupling storage and compute, Iceberg enables the use of various processing engines and storage systems, as long as they understand Iceberg's metadata.

Iceberg introduces features like data versioning, asset transactions, schema evolution, and more, enhancing data governance.

Iceberg's efficiency, flexibility, and feature-rich capabilities come with minimal overhead due to its clever metadata organization.

As data continues to grow with the AI boom, Iceberg's popularity for modern data management is evident, especially in the mid-2020s.

Involvement in the open-source community, such as with Iceberg, is encouraged to drive continuous improvement and innovation.

Transcripts

You may have heard of the term "big data",

but why is that important?

The answer you get today might be something along the lines of the fact

that a huge amount of data is required to train, tune and evaluate

the A.I. models that are the future of computing.

But managing all of this data can be really difficult.

Luckily for us, we have the open source Project Apache Iceberg

to make things much easier.

In this video, I'll be taking you through a brief history of Big Data

and its challenges and solutions of the last two decades

so that you can walk away with an understanding of why Apache Iceberg

is such a great choice for modern data management.

But before we get into that, let's define what a data management system is.

We can think about it

in terms of a library,

a library similar to big data stores, more content than ever before.

Not just in physical books, but in digital storage as well.

And that's the first component of our library.

We need a good amount of storage

for all of these different types of content.

The second component is some sort of processing power.

So some way to satisfy the library visitors requests.

And in a library we can sort of think as the librarian, as the processing power.

We also need to keep some sort of metadata,

which would be information on how the content of the library is organized.

So maybe they use the Dewey Decimal System.

It might also store some metadata on that metadata.

And this can provide something

like a historical record of the library's contents over time.

So, of course, these components do not just apply to a library.

They really apply to any data management system.

The only difference is the scale at which they work.

So organizations that do a lot of data processing today

are doing so at a much larger scale than a library is.

Hence the term "big data".

And big data is getting even bigger all the time.

So let's go back to the dawn of big data

to see how the problem has evolved over time so that we can frame

our discussion on why Apache Iceberg is such a great choice.

So we'll start in the early 2000.

And this, of course, is the adolescence of the Internet.

Thanks to the Internet, we're now processing more data than ever before.

And it's, of course, much more data than a single machine is capable of.

So in 2005, in order to address this,

Apache Hadoop is open sourced and it provides a multi machine architecture.

It's composed of two main parts.

First is a set of on-prem distributed machines

called the Hadoop Distributed file System.

It also has a parallel processing model called MapReduce

that processes the underlying data.

So this is cool because it's easier to just add a machine

to our cluster whenever the volume of data that we're working with scales up.

But there is a pain points, and that is with MapReduce.

MapReduce jobs are essentially Java programs

and they're much more difficult to write when compared with the simple

one line SQL statements that a data analyst would be more familiar with.

So this would be like going to a library in order to find a particular book.

But when you get there, you find that you and the librarian speak different languages.

We clearly have a bit of a bottleneck at the processing stage,

but a few years later, in 2008,

Apache Hive comes onto the scene. In order to solve this problem.

Its main draw is its ability to translate SQL like queries into MapReduce jobs.

But it comes with a bonus feature as well. And that is the Hive Metastore.

This is meta database

that essentially stores pointers to certain groups of files

in the underlying file system.

So now when a query is submitted, it's done so in SQL,

Hive accesses it's meta store to optimize this query

before it's finally sent off to MapReduce.

So taking it back to our library example again,

we now have a pocket translator

that we can use to speak to the librarian.

The librarian also has a cheat sheet

that they can use to find where a particular genre of book is stored in its shelves.

So this works very well for a while until the 2010's.

And at this point, we have another problem of scale.

The reason for this is we have more mobile devices than ever before.

So we have a lot of smartphones, we have a lot of Internet of Things devices,

and they're all producing more data than ever.

To handle this increase in the amount of data.

Organizations are more and more turning to cloud based S3 storage.

The reason being that S3 storage is much more affordable

and even easier to scale than in DFS would be.

Unfortunately, Hive cannot talk to S3 storage.

It can only talk to HDFC, but there is another problem as well.

More and more, instead of doing the traditional scheduled batch processing

that was more popular, we're now doing a lot more on demand, real time processing.

Like what something like the Presto query engine can do.

And Hive is just too slow for this use case.

So we have two problems,

but unfortunately there's a third as well.

And organizations don't really want to start from scratch

with their data management system.

They still have a lot of storage of data in HDFC, and that processing is certainly not obsolete.

It has its place in the ecosystem.

So perhaps they want to run some batch jobs using their existing hive instance

or a query engine like Apache Spark.

So luckily for us, we don't have to wait too long for a solution.

All of these problems in 2017,

Apache Iceberg is open sourced

and it promises not only to solve all of these problems,

but also to introduce new features of its own.

Iceberg is really interesting because essentially,

rather than providing its own storage and compute layers,

it's simply a layer of metadata in between.

So like in Hive, Iceberg's metadata contains a picture of how the underlying storage is organized.

But Iceberg, however, keeps a much more fine grained picture than Hive does.

So if we compare it to our library example,

now that we're using Apache Iceberg,

our library is more like one that has a makes use of the Dewey Decimal System

and has a very organized index to keep track of all of that.

As you can imagine, that means requests are processed much faster,

but it's not just more efficient.

Iceberg's metadata makes it more flexible as well.

Since we're essentially decoupling the storage and the compute

using this extra layer of separation of the metadata,

we now have the flexibility to query

using any number of processing engines

and to access data in any number of underlying storage systems.

The only requirement is that all the pieces of the ecosystem understand Iceberg's metadata language.

So again, taking it back to our library example,

rather than having the single librarian who does not speak our language,

the library has kindly hired several more librarians that speak a variety of languages.

Their key qualification is, of course, that they can understand the libraries index.

And as I mentioned, the index itself is a lot more detailed.

So not only can we point to the physical shelves of the library,

we can also point to the digital content as well.

But Iceberg is more than just efficient and flexible.

It provides several new features of its own,

mostly in the realm of data governance.

With Iceberg, you can do data versioning operations,

asset transactions, schema evolution, partition evolution, and more.

And initially it sounds like that would require a lot of extra infrastructure in order to support.

But in fact it is thanks to an extra layer of metadata that Iceberg keeps,

and this time the metadata is meta-metadata.

So Iceberg essentially takes snapshots of our data at particular points in time.

And this is what allows us to have a really fine grained control

over the integrity and the consistency of our data.

So let's bring it back one last time to our library.

Say, in our library, we want to add a historical record of the contents over time.

Well, we already have the pretty detailed index that we keep.

It's actually not that much extra information that we have to store

in order to tell, for example, when a particular piece of content was added to the collection.

So we now have data governance features

with only needing to store one extra field in our index.

And, as much as this is not a lot of extra information, it is a big impact change.

And this is really the theme of Iceberg overall.

Due to the clever way that it organizes its metadata,

Iceberg is efficient, flexible and feature rich,

all with very little relative overhead.

So now as we move into the mid 2020s

and as data is getting even bigger thanks to this AI boom,

it becomes clear why Iceberg continues to be such a popular choice for modern data management.

So now that you know what Iceberg is,

I would really encourage you to go out and get involved.

Like all open source communities,

Iceberg will only continue to improve,

the more people that participate in the discussion.

So thank you for watching and I hope to see you out there on the open source world.