00:07Hey y'all, data guy here

00:14Um, and so today I'm kind of following up on my Apache Hudi video, um, and wanted to have a second video also going through, it's kind of, you know, competitor within this open data lakehouse space, the Apache projects that are covering kind of the data lakehouse niche, we have Apache Iceberg

00:21Um, and so want to explore what Apache Iceberg is, how it works, give you some of the main use cases it was developed for, the cases it's used for now, some of the biggest companies that kind of collaborate with it, hint, Snowflake's one of them, uh, and then go into some of the pros and cons of using Apache Iceberg

00:37Probably in preparation for a future comparison video between Iceberg and Hoodie, uh, but for now, just wanted to cover Iceberg, give you kind of a standalone video so that you can come here and get a good understanding of what Iceberg is, how it works, and if it's the right tool for your particular use case

00:54So without further ado, let's get into it

00:56So at its most basic level, Apache Iceberg is really just a high-performance table format that's used for handling really large analytic data sets

01:06It was first developed by Netflix, but then was adopted by the Apache Software Foundation, and it aims to address the Limitations of traditional data table formats in really big data environments, specifically, you know, a lot of times in data lakehouse environments, and offers robust schema evolution, partitioning, and metadata management capabilities

01:24So, kind of like some high-level, you know, ideas of, you know, how exactly Apache Iceberg works and how it kind of differentiates itself from the competition

01:33Uh, number one, you have things like table format and metadata management handled at the file level, so Iceberg tables store data in columnar file formats, so like Parquet, Avro, or ORC, and the key differentiator here from you

01:48know just a standard parquet file is that it has an advanced metadata layer, which contains the table state without the need for a centralized catalog service, so it makes it easier to quickly read and understand the contents of the table without needing to have a centralized catalog that contains all the information around all of your tables

02:05Um, and then Iceberg also uses snapshot-based metadata management, so you have safe and consistent reads and writes, because if you know something happens, there's a bad write, something corrupts, you can go back to previous snapshots of that particular table

02:18Because you have that metadata that contains all those past transformations that you can then roll back

02:21So some other key features, um, are schema evolution

02:26You know, you have Iceberg support schema evolution without requiring any kind of costly rewrites of existing data

02:32Um, and so users can add, drop, rename columns, make really any kind of schema changes very easily

02:38Um, and then you have kind of resiliency built in because you have those version snapshots I mentioned before that capture the state of that table at any point in time, and so you can use that to effectively keep, have a catalog of all your schema

02:51Changes and kind of have change data capture built into your tables, and you know, so schema changes don't involve needing to re-add or needing to rewrite and rebuild your entire table

03:03Another really big advantage of Apache Iceberg is its flexible partitioning strategies, so it goes beyond just kind of traditional Hive-style partitions and actually embeds partitioning information in metadata, which really helps to enable efficient query pruning and then also reducing the amount of data scanned during queries

03:23And then finally, you have snapshot isolation and time travel, so again because you Have that snapshot-based architecture that you can see a consistent view of the data over time

03:32Um, so you know, readers are able to constantly pull from a data set, versus your writers, people are sending data into that data set, can make changes without worrying that it's going to affect those downstream readers

03:42And so this also enables features like time travel, where because you have all these, this record of changes, you can roll back those changes and go to, or just go back to a previous snapshot of that table and query data as if it just existed at that previous snapshot

03:57Um, so really good for use cases where you

04:00Need to go back and capture historical records and information, so now you kind of have an idea of how Apache Iceberg works at the high level

04:08I want to talk about how it's used in the real world, and so just kind of where you should think of Apache Iceberg as sitting within most data stacks is there kind of a layer above all of your cloud storage

04:19Um, so it manages the interaction, you know, the storage of data within there, within that special Iceberg format, um, and it, you know, stores it wherever cloud storage bucket uh you actually want to use for supporting your data lakehouse storage

04:34Um, and the reason why I keep Talking about data lakehouses is because, as Iceberg is really, really well tuned towards data lakehouse architectures, it's ideal for data lakehouse environments where the goal is, you know, combining flexibility and also scalability of data lakes, but having some of the more, you know, complex management performance features of data warehouses, and as it also helps provide robust ACID transactions, so it makes it really well suited for enterprise data lakes where you need to have a little bit more control and understanding of what's going on in the data lake, rather than, you know, maybe someone

05:08That's just starting out and just throwing everything into a data lake, you know, if you're a startup, you're not going to worry about wasting a lot of time developing these processes, but you know, when you're at really large organizations, you got to be really strict about your data

05:19Um, and so Apache Iceberg is able to handle, you know, both stream and batch processing workloads as well, very seamlessly

05:26Uh, so as you can see, you know, we have streaming, you know, things like Flink, but also things like Spark, um, that can both leverage Iceberg because Iceberg has the ability to manage incremental data updates, perform Snapshot-based queries, which is really perfect for either real-time analytics or ETL processes, um, and has that kind of optionality when you're using it, because, hey, you know, I can get the most latest data right when it's generated, or, you know, maybe I don't trust that latest data, I need it, I want it to be clean first, so I'm actually going to take a snapshot of, you know, one or two days behind, and you have that ability within Apache Iceberg to even do both those at the same time

06:05Additionally, the really detailed metadata management structure and the ability to evolve schemas over time make it a really great

06:12Fit for environments that have strict data governance and compliance requirements, because it's going to help ensure that you have data integrity, and then also when the auditors come in, you have a way to have easy auditing and tracking of all the different changes, everything that's happened in your data over a really long time period, and the ability to, you know, roll back and go to a particular point in time that someone might be requesting for a report

06:33Um, and then finally, advanced analytics

06:36Um, Iceberg has really efficient partitioning improving capabilities, which make it a great choice for Advanced analytics workloads, because it enables users to run complex queries on large data sets without incurring really significant performance penalties, because you have that efficient partitioning where, hey, I don't need to look across every single object that's stored within my object storage

06:57Iceberg is able to take that query and partition it down to just a specific subset of that that it needs to run in query to get that information

07:05So now I want to kind of explore some of the pros and cons that you might run into, what if you choose Apache Iceberg as your, you know, data lake house provider

07:16Or data lake provider, number one in terms of pros, is that schema evolution capability I mentioned before

07:23The ability to handle schema changes without needing to, you know, run expensive rewrites is a really significant advantage, um, because it means you don't have to spend a lot of manpower and time working through these really large rewrites that are, you know, both computationally expensive, and then it's also going to be manpower expensive because you have to, you know, it's a lot of mental, not a lot, but it can be a large mental burden to have to figure out how to rewrite and reconcile all these different

07:49needs when you're handling schema changes, and so having the ability that's just built in to handle scheme evolution really helps to ensure flexibility, and then reduce that maintenance overhead over time

07:58Um, additionally, those asset transactions I mentioned before, Iceberg supports full asset transactions, providing that data consistency, that really strong reliability that comes with asset transactions, and that can be crucial for some enterprise applications, so it's kind of a no-brainer to go with Iceberg for applications that require that, you also have, you know, really great

08:21Scalability, um, Iceberg is really designed for large-scale data sets and can handle, you know, petabytes of data efficiently, uh, making it really suitable for those big data applications where you need to just, you know, throw the kitchen sink and everything else into that data lake

08:34Um, it's also a very interoperable engine

08:37Um, it's compatible with Spark, Flink, Trino, Hive, you saw kind of all those different examples up on the screen earlier, there are not a lot of situations where you won't have the ability to integrate with Apache Iceberg, um, for, you know, obviously relevant solutions, and so that offers you a

08:51lot of flexibility and choosing the right tool for the job outside of just Apache Iceberg, and then finally those that efficient querying, I mentioned, you know, those advanced partitioning and pruning techniques, you really have, you really are ensuring efficient querying and reducing the amount of data scanned, and then also, you know, having that downstream effect of also improving query performance because you have those that partition data that's easier to search through and query

09:15Now, on the downside of things, there is a pretty decent amount of complexity that comes with implementing and

09:23Managing Iceberg, um, especially if you haven't worked with some of those formats like Parquet and Avro, and Big Data ecosystems, it's a lot to take in all at once

09:32So, starting with, you know, just learning how Parquet works, how Avro works, how, you know, these more kind of complex data table formats work is really crucial to being successful with Iceberg

09:43And then it's also pretty resource intensive

09:45Um, you know, you save a lot on the queries, but the metadata management and snapshot isolation features and storing all the snapshots can be really resource intensive and require a lot more compute and storage overhead

09:57Uh, compared to simpler data formats

10:00Additionally, um, you know, while Iceberg supports many different processing engines, integrating and actually maintaining those systems and making them work nicely with Iceberg can be pretty significant operational overhead, and is going to require you to, you know, get your hands dirty and build out some integrations and how it works together, because it's not always super smooth, um, integrating them

10:20And then also the community ecosystem maturity, you know, Iceberg is getting super popular in the ecosystem, you know, you got Snowflake that really took a Shining to it and has implemented as its, you know, standard data lake format, but its computer and ecosystem are still a little bit less developed than more Pat or more established solutions like Apache Hudi, or Delta Lake, so just something to keep in mind

10:45You know, if you're trying to implement this, you know, really open source, you might not have as much community resources to lean on as you might expect

10:51Um, but anyways, that is kind of my intro primer to Apache Iceberg

10:57Hope you've enjoyed this video, hope you have a great rest of your day

10:59Day to gu.