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new c8741e9bacc [DOCS] Blog - 21 reasons, why Hudi (#12922)
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Author: vinoth chandar <[email protected]>
AuthorDate: Wed Mar 5 16:20:44 2025 -0800
[DOCS] Blog - 21 reasons, why Hudi (#12922)
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+---
+title: "21 Unique Reasons Why Apache Hudi Should Be Your Next Data Lakehouse"
+excerpt: "Unique Differentiators of Apache Hudi, that stand out from other
projects"
+author: Vinoth Chandar
+category: blog
+image: /assets/images/blog/2025-03-05-21-reasons-why.png
+tags:
+- Data Lake
+- Data Lakehouse
+- Apache Hudi
+- Apache Iceberg
+- Delta Lake
+- Table Format
+---
+
+Apache Hudi is continuously
[redefining](https://hudi.apache.org/blog/2024/12/16/announcing-hudi-1-0-0) the
data lakehouse, pushing the technical boundaries and offering cutting-edge
features to handle data quickly and efficiently. If you have ever wondered how
Apache Hudi has sustained its position over the years as the most
comprehensive, open, high-performance data lakehouse project, this blog aims to
give you some concise answers. Below, we shine a light on some unique
capabilities i [...]
+
+**1\. Well-Balanced Storage Format**
+
+Hudi’s [storage format](https://hudi.apache.org/docs/storage_layouts)
*perfectly balances write speed* (record-level changes) and *query performance*
(scan+lookup optimized), at the cost of additional storage space to track
indexes. In contrast, Apache Iceberg/Delta Lake formats produce storage layouts
aimed at vanilla scans, focus more on metadata to help scale/prune the scans.
Recent effots that adopt LSM tree structures to improve write performance,
inevitably sacrifice query performa [...]
+
+**2\. Database-like Secondary Indexes**
+
+In a long line of unique technical contributions to the lakehouse tech, Hudi
recently added [secondary
indexes](https://hudi.apache.org/docs/indexes#multi-modal-indexing) (record
level, bloom filters, …), with support for even creating indexes on expressions
on columns. Features heavily inspired by relational databases like Postgres,
that can *unlock completely new use-cases* on the data lakehouse like
[HTAP](https://en.wikipedia.org/wiki/Hybrid_transactional/analytical_processing)
or [i [...]
+
+**3\. Efficient Merge-on-Read (MoR) Design**
+
+Hudi’s [optimized MoR
design](https://hudi.apache.org/docs/table_types#merge-on-read-table)
*minimizes read/write amplification*, by a range of techniques like file
grouping and partial updates. Grouping helps cut down the amount of update
blocks/deletion blocks/vectors to be scanned to serve snapshot queries. It also
helps *preserve temporal locality* of data that dramatically improves
time-based access for e.g building dashboards based on time \- last hour, last
day, last week, … \- th [...]
+
+**4\. Scalable Metadata for Large-Scale Datasets**
+
+Hudi’s [metadata table](https://hudi.apache.org/docs/metadata) efficiently
handles *millions of files*, by storing them *efficiently* in an indexed
[SSTable](https://www.scylladb.com/glossary/sstable) based file format.
Similarly, Hudi also indexes other metadata like column statistics, such that
query planning scales linearly with *O(number\_of\_columns\_in\_query)*, as
opposed to flat-file storage like avro that scales poorly with size of tables,
large number of files or wide-columns.
+
+**5\. Built-In Table Services**
+
+Hudi comes *loaded with automated [table
services](https://hudi.apache.org/docs/write_operations#write-path)* like
compaction, clustering, indexer, de-duplication, archiver, TTL enforcement and
cleaning, that are scheduled, executed, retried, automatically with every write
without requiring any external orchestration or manual SQL commands for table
maintenance. Hudi’s [marker mechanism](https://hudi.apache.org/docs/markers/)
efficiently cleans up uncomitted/orphaned files during writes [...]
+
+**6\. Data Management Smarts**
+
+Stepping in level deeper, Hudi fully manages everything around storage : [file
sizes, partitions and metadata
maintenance](https://hudi.apache.org/docs/overview) automatically on each
write, to provide consistent, dependable read/write performance. Further more,
Hudi provides *advanced
[sorting/clustering](https://hudi.apache.org/docs/clustering) capabilities*,
that can be *incrementally* run with new writes, to keep tables optimized.
+
+**7\. Concurrency Control Purpose-built For the Lake**
+
+Hudi’s [concurrency
control](https://hudi.apache.org/blog/2025/01/28/concurrency-control) is
carefully designed to deliver high throughput for data lakehouse workloads,
without blindly rehashing approaches that work for OLTP databases. Hudi brings
novel MVCC based approaches and [non-blocking concurrency
control](https://hudi.apache.org/docs/concurrency_control#non-blocking-concurrency-control).
Data pipelines/SQL ETLs and table services won’t fail/livelock each other
eliminating wastage [...]
+
+**8\. Performance at Scale**
+
+Hudi stands out on the *toughest workloads* you should be testing first before
deciding your lakehouse stack : CDC ingest, expensive SQL merges or TB-PB scale
streaming data. Hudi provides about [half a dozen writer side
indexes](https://hudi.apache.org/docs/indexes#additional-writer-side-indexes)
including advanced record level indexes, range indexes built on interval trees
or consistent-hashed bucket indexes to scale writes for such workloads. Hudi is
the *only lakehouse project*, that [...]
+
+**9\. Out-of-box CDC/Streaming Ingestion**
+
+Hudi provides *powerful, fully-production ready ingestion*
[tools](https://hudi.apache.org/docs/hoodie_streaming_ingestion) for both
Spark/Flink/Kafka users, that help users build data lakehouses from their data,
with a single-command. In fact, many many Hudi users blissfully use these
tools, unaware of all the underlying machinery balancing write/read performance
or table maintenance. This way, Hudi provides a self-managing runtime
environment, for your data lakehouse pipelines, withou [...]
+
+**10\. First-Class Support for Keys**
+
+Hudi treats record [keys](https://hudi.apache.org/docs/key_generation) as
first-class citizen, used everywhere from indexing, de-duplication, clustering,
compaction to consistently track/control movement of records within a table,
across files. Additionally, Hudi also tracks [necessary record-level
metadata](https://www.onehouse.ai/blog/hudi-metafields-demystified) that help
implement powerful features like incremental queries, in conjunction with
queries. Ingest tools seamlessly map sou [...]
+
+**11\. Streaming-First Design**
+
+Hudi was born out of a need to bridge the gap between batch processing and
stream processing models. Thus, naturally, Hudi offers *best-in-class and
unique capabilities* around handling streaming data. Hudi supports [event time
ordering](https://hudi.apache.org/docs/record_merger#event_time_ordering) and
late data handling natively in storage where MoR is employed heavily.
RecordPayload/RecordMerger APIs let you merge updates in the database LSN order
compared to other approaches, avoidi [...]
+
+**12\. Efficient Incremental Processing**
+
+All roads in Hudi, lead to efficiency in storage and compute. Storage by
*reducing* the amount of *data stored/accessed*, compute by reducing the *time
needed write/read*. Hudi supports unique [incremental
queries](https://www.onehouse.ai/blog/getting-started-incrementally-process-data-with-apache-hudi),
along with CDC queries to allow downstream data consumers to quickly obtain
changes to a table, between two time intervals. Owing to scalable metadata
design, a LSM-tree backed timeline [...]
+
+**13\. Powerful Apache Spark Implementation**
+
+Hudi comes with a very feature-rich, advanced integration with Apache Spark \-
across SQL, DataSource, RDD APIs, Structured Streaming and Spark Streaming.
When combined together, *Hudi \+ Spark* almost gives users a
[database](https://github.com/apache/hudi/blob/master/rfc/rfc-69/rfc-69.md) \-
with built-in data management, ingestion, streaming/batch APIs, ANSI SQL and
programmatic access from Python/JVM. Much like a database, the write/read
implementation paths automatically pick the ri [...]
+
+**14\. Next-Gen Flink Writer for Streaming Pipelines**
+
+[Hudi and Flink](https://www.onehouse.ai/blog/intro-to-hudi-and-flink) have
the best impedance match when it comes to handling streaming data. Hudi Flink
sink is built on a *deep integration* between the two project capabilities, by
leveraging Flink’s state backends as an writer side index in Hudi. With the
combination of non-blocking concurrency and partial updates, Hudi is the only
lakehouse storage sink for Flink, that can allow *multiple streaming writers*
concurrently write a table [...]
+
+**15\. Avoid Compute Lockins**
+
+Don’t let the noise fool you. Hudi is [*widely
supported*](https://hudi.apache.org/ecosystem) across cloud warehouses
(Redshift, BigQuery), open-source query/processing engines (Spark, Presto,
Trino, Flink, Hive, Clickhouse, Starrocks, Doris) and also hosted offering of
those open-source engines (AWS Athena, EMR, DataProc, Databricks). This means,
you have the power to fully control *not just the open format* you store data
in, but also the end-end ingestion, transformation and optimizat [...]
+
+**16\. Seamless Interop Iceberg/Delta Lake and Catalog Syncs**
+
+To make the point above really easy, Hudi also ships with a [catalog
sync](https://hudi.apache.org/docs/syncing_aws_glue_data_catalog) mechanism,
that supports about *6 different data catalogs* to keep your table definitions
in sync over time. Hudi tables can be readily queried as external tables on
cloud data warehouses. And, with the [Apache
XTable](https://github.com/apache/xtable) (Incubating) catalog sync, Hudi
enables interoperability with Iceberg and Delta Lake table format, witho [...]
+
+**17\. Truly Open and Community-Driven**
+
+Apache Hudi is an [open-source project](https://hudi.apache.org/community),
actively developed by a diverse global
[community](https://ossinsight.io/analyze/apache/hudi#contributors). In fact,
the grass-roots nature of the project and its community have been the crucial
reason for the lasting success Hudi has had in the industry, inspite 100-1000x
bigger vendor teams marketing/selling users in other directions. Project has an
established track record of truly, collaborative way of softwa [...]
+
+**18\. Massive Adoption Across Industries**
+
+For system/infrastructure software like Hudi, it’s very important to
gain/prove maturity by clocking massive amounts of server hours. Hudi is used
at massive scale at much of the Fortune 100s and large organizations like
[Uber, AWS, ByteDance, Peloton, Huawei, Alibaba, and
more](https://hudi.apache.org/powered-by), adding immense value in terms of a
steady stream of high-quality bug reports and feature asks shaping the
projects roadmap. This way, Hudi users get highly capable lakehouse [...]
+
+**19\. Proven Reliability in High-Pressure Workloads**
+
+Hudi has been pressure-tested at some of the most demanding worloads there is,
on the data lakehouse. From [minute-level
latency](https://www.uber.com/blog/uber-big-data-platform/) on petabytes to
storing ingesting \> 100GB/s or just very [tough random
write](https://aws.amazon.com/blogs/big-data/how-amazon-transportation-service-enabled-near-real-time-event-analytics-at-petabyte-scale-using-aws-glue-with-apache-hudi/)
workloads, that test even the best OLTP databases out there. Hudi has [...]
+
+**20\. Cloud-Native and Lakehouse-Ready**
+
+Don’t let the origins from a Hadoop mislead you either. Hudi has long evolved
past HDFS and works seamlessly with [S3, GCS, Azure, Alibaba, Huawei and many
other cloud storage](https://hudi.apache.org/docs/cloud) systems. Together with
the
[cloud-native](https://www.onehouse.ai/blog/apache-hudi-native-aws-integrations)
integrations or just via [easy
integrations](https://www.onehouse.ai/blog/apache-hudi-on-microsoft-azure)
outside of Cloud-native services, Hudi provides a very portable ( [...]
+
+**21\. Future-Proof and Actively Evolving**
+
+Hudi’s community boasts about 40-50 monthly active developers, which is
growing even more with efforts like
[hudi-rs](https://github.com/apache/hudi-rs). Hudi’s [rapid
development](https://github.com/apache/hudi) ensures constant improvements and
cutting-edge features on one hand, while the openness of the community to truly
work across the entire cloud data ecosystem on the other, ensure your data
stays as open as possible.
+
+In summary, there is no secret sauce. The answer to the original question is
simply how these design and implementation differences have compounded over
time into unmatched technical capabilities that data engineers across the
industry widely recognize. These have resulted from 6+ years of evolution,
hardening and iteration from an OSS community. And, it's always a moving
target, given the amount of innovation that is still ahead of us, in the data
lakehouse space. By the time some of th [...]
+
+Apache Hudi is the **best-in-class open-source data lakehouse platform**
—powerful, efficient, and future-proof. Start exploring it today\! 🚀
+
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