Apache Hudi Description
Hudi is a rich platform for building streaming data lakes using incremental data pipelines on a self managing database layer. It can also be optimized for regular batch processing and lake engines. Hudi keeps a timeline of all actions on the table at different times. This allows for instantaneous views and efficient retrieval of data in the order they were received. The following components make up a Hudi instant. Hudi provides efficient upserts by mapping a given Hoodie key consistently with a file ID, via an indexing mechanism. Once a record is written to a file, the mapping between record key/file group/file ID never changes. The mapped file group includes all versions of a group record.
Apache Hudi Alternatives
VeloDB
VeloDB, powered by Apache Doris is a modern database for real-time analytics at scale.
In seconds, micro-batch data can be ingested using a push-based system. Storage engine with upserts, appends and pre-aggregations in real-time. Unmatched performance in real-time data service and interactive ad hoc queries.
Not only structured data, but also semi-structured. Not only real-time analytics, but also batch processing. Not only run queries against internal data, but also work as an federated query engine to access external databases and data lakes.
Distributed design to support linear scalability. Resource usage can be adjusted flexibly to meet workload requirements, whether on-premise or cloud deployment, separation or integration.
Apache Doris is fully compatible and built on this open source software. Support MySQL functions, protocol, and SQL to allow easy integration with other tools.
Learn more
Apache Doris
Apache Doris is an advanced data warehouse for real time analytics. It delivers lightning fast analytics on real-time, large-scale data.
Ingestion of micro-batch data and streaming data within a second. Storage engine with upserts, appends and pre-aggregations in real-time.
Optimize for high-concurrency, high-throughput queries using columnar storage engine, cost-based query optimizer, and vectorized execution engine.
Federated querying for data lakes like Hive, Iceberg, and Hudi and databases like MySQL and PostgreSQL.
Compound data types, such as Arrays, Maps and JSON. Variant data types to support auto datatype inference for JSON data. NGram bloomfilter for text search.
Distributed design for linear scaling. Workload isolation, tiered storage and efficient resource management. Supports shared-nothing as well as the separation of storage from compute.
Learn more
Apache Iceberg
Iceberg is an efficient format for large analytical tables. Iceberg brings the simplicity and reliability of SQL tables to the world of big data. It also allows engines like Spark, Trino Flink Presto Hive Impala and Impala to work safely with the same tables at the same time. Iceberg supports SQL commands that are flexible to merge new data, update rows, and perform targeted deletions. Iceberg can eagerly write data files to improve read performance or it can use delete-deltas for faster updates. Iceberg automates the tedious, error-prone process of generating partition values for each row in a table. It also skips unnecessary files and partitions. There are no extra filters needed for fast queries and the table layout is easily updated when data or queries change.
Learn more
Dremio
Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.
Learn more
Integrations
Company Details
Company:
Apache Corporation
Year Founded:
1954
Headquarters:
United States
Website:
hudi.apache.org
Media
Recommended Products
Our Free Plans just got better! | Auth0 by Okta
You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your secuirty. Auth0 now, thank yourself later.
Product Details
Platforms
SaaS
Type of Training
Documentation
Customer Support
Online
Apache Hudi Features and Options
Apache Hudi User Reviews
Write a Review- Previous
- Next