Apache Iceberg: Solving Modern Big Data
Key Points
- Big data is essential for training, tuning, and evaluating modern AI models, but its sheer volume makes management increasingly complex.
- A data management system can be likened to a library that needs ample storage, processing power (the “librarian”), and rich metadata to organize and retrieve content at scale.
- Since the early 2000s, technologies like Apache Hadoop introduced distributed storage (HDFS) and parallel processing (MapReduce) to handle data that outgrows single machines.
- MapReduce, while powerful, required Java programming and proved cumbersome for analysts accustomed to simple SQL queries, highlighting a usability gap.
- Apache Iceberg addresses these challenges by offering an open‑source, modern data‑management layer that simplifies handling, querying, and evolving massive datasets.
Sections
- Big Data Challenges and Apache Iceberg - The speaker explains why massive data sets are vital for AI, outlines the difficulties of managing them, and introduces the open‑source Apache Iceberg as a modern solution, using a library analogy to illustrate storage, processing, and metadata management.
- Hadoop, MapReduce, and Hive Evolution - The passage explains how Hadoop’s distributed storage and MapReduce processing introduced scalable big‑data handling but were hard for analysts, leading to Hive’s 2008 debut, which converts SQL‑like queries into MapReduce jobs and uses a metastore to optimize access.
- Bridging Storage and Query Gaps - The speaker explains how soaring mobile/IoT data pushes firms to adopt cheap, scalable S3 storage, yet Hive’s inability to access S3 and its sluggish batch‑only performance hinder real‑time analytics, prompting the 2017 open‑source release of Apache Iceberg to unify S3 compatibility with both batch and interactive query workloads.
- Iceberg's Metadata-Driven Flexibility - Iceberg decouples storage and compute via a rich metadata layer, enabling any engine to query any storage while offering governance features like versioning, transactions, and schema evolution through snapshot metadata.
- Encouraging Community Participation in Iceberg - The speaker emphasizes that Iceberg's growth depends on active community involvement, thanks viewers, and invites them to join the open‑source ecosystem.
Full Transcript
# Apache Iceberg: Solving Modern Big Data **Source:** [https://www.youtube.com/watch?v=6tjSVXpHrE8](https://www.youtube.com/watch?v=6tjSVXpHrE8) **Duration:** 00:12:42 ## Summary - Big data is essential for training, tuning, and evaluating modern AI models, but its sheer volume makes management increasingly complex. - A data management system can be likened to a library that needs ample storage, processing power (the “librarian”), and rich metadata to organize and retrieve content at scale. - Since the early 2000s, technologies like Apache Hadoop introduced distributed storage (HDFS) and parallel processing (MapReduce) to handle data that outgrows single machines. - MapReduce, while powerful, required Java programming and proved cumbersome for analysts accustomed to simple SQL queries, highlighting a usability gap. - Apache Iceberg addresses these challenges by offering an open‑source, modern data‑management layer that simplifies handling, querying, and evolving massive datasets. ## Sections - [00:00:00](https://www.youtube.com/watch?v=6tjSVXpHrE8&t=0s) **Big Data Challenges and Apache Iceberg** - The speaker explains why massive data sets are vital for AI, outlines the difficulties of managing them, and introduces the open‑source Apache Iceberg as a modern solution, using a library analogy to illustrate storage, processing, and metadata management. - [00:03:06](https://www.youtube.com/watch?v=6tjSVXpHrE8&t=186s) **Hadoop, MapReduce, and Hive Evolution** - The passage explains how Hadoop’s distributed storage and MapReduce processing introduced scalable big‑data handling but were hard for analysts, leading to Hive’s 2008 debut, which converts SQL‑like queries into MapReduce jobs and uses a metastore to optimize access. - [00:06:15](https://www.youtube.com/watch?v=6tjSVXpHrE8&t=375s) **Bridging Storage and Query Gaps** - The speaker explains how soaring mobile/IoT data pushes firms to adopt cheap, scalable S3 storage, yet Hive’s inability to access S3 and its sluggish batch‑only performance hinder real‑time analytics, prompting the 2017 open‑source release of Apache Iceberg to unify S3 compatibility with both batch and interactive query workloads. - [00:09:23](https://www.youtube.com/watch?v=6tjSVXpHrE8&t=563s) **Iceberg's Metadata-Driven Flexibility** - Iceberg decouples storage and compute via a rich metadata layer, enabling any engine to query any storage while offering governance features like versioning, transactions, and schema evolution through snapshot metadata. - [00:12:32](https://www.youtube.com/watch?v=6tjSVXpHrE8&t=752s) **Encouraging Community Participation in Iceberg** - The speaker emphasizes that Iceberg's growth depends on active community involvement, thanks viewers, and invites them to join the open‑source ecosystem. ## Full Transcript
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.