Hudi atomically maps keys to single file groups at any given point in time, supporting full CDC capabilities on Hudi tables. Lets focus on Hudi instead! The DataGenerator tripsIncrementalDF.createOrReplaceTempView("hudi_trips_incremental"), spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from hudi_trips_incremental where fare > 20.0").show(). Clients. Hudi also supports scala 2.12. The unique thing about this There, you can find a tableName and basePath variables these define where Hudi will store the data. For example, records with nulls in soft deletes are always persisted in storage and never removed. Once the Spark shell is up and running, copy-paste the following code snippet. // It is equal to "as.of.instant = 2021-07-28 00:00:00", # It is equal to "as.of.instant = 2021-07-28 00:00:00", -- time travel based on first commit time, assume `20220307091628793`, -- time travel based on different timestamp formats, val updates = convertToStringList(dataGen.generateUpdates(10)), val df = spark.read.json(spark.sparkContext.parallelize(updates, 2)), -- source table using hudi for testing merging into non-partitioned table, -- source table using parquet for testing merging into partitioned table, createOrReplaceTempView("hudi_trips_snapshot"), val commits = spark.sql("select distinct(_hoodie_commit_time) as commitTime from hudi_trips_snapshot order by commitTime").map(k => k.getString(0)).take(50), val beginTime = commits(commits.length - 2) // commit time we are interested in. Typical Use-Cases 5. Note that working with versioned buckets adds some maintenance overhead to Hudi. Users can set table properties while creating a hudi table. For each record, the commit time and a sequence number unique to that record (this is similar to a Kafka offset) are written making it possible to derive record level changes. specific commit time and beginTime to "000" (denoting earliest possible commit time). specific commit time and beginTime to "000" (denoting earliest possible commit time). Apache Hudi is a transactional data lake platform that brings database and data warehouse capabilities to the data lake. Hudi can query data as of a specific time and date. AWS Fargate can be used with both AWS Elastic Container Service (ECS) and AWS Elastic Kubernetes Service (EKS) Hudi isolates snapshots between writer, table, and reader processes so each operates on a consistent snapshot of the table. To take advantage of Hudis ingestion speed, data lakehouses require a storage layer capable of high IOPS and throughput. Apache Iceberg is a new table format that solves the challenges with traditional catalogs and is rapidly becoming an industry standard for managing data in data lakes. In this first section, you have been introduced to the following concepts: AWS Cloud Computing. option(QUERY_TYPE_OPT_KEY, QUERY_TYPE_INCREMENTAL_OPT_VAL). Soumil Shah, Dec 30th 2022, Streaming ETL using Apache Flink joining multiple Kinesis streams | Demo - By When the upsert function is executed with the mode=Overwrite parameter, the Hudi table is (re)created from scratch. Hudi analyzes write operations and classifies them as incremental (insert, upsert, delete) or batch operations (insert_overwrite, insert_overwrite_table, delete_partition, bulk_insert ) and then applies necessary optimizations. option("as.of.instant", "2021-07-28 14:11:08.200"). We will use the default write operation, upsert. Hive is built on top of Apache . Another mechanism that limits the number of reads and writes is partitioning. To use Hudi with Amazon EMR Notebooks, you must first copy the Hudi jar files from the local file system to HDFS on the master node of the notebook cluster. Modeling data stored in Hudi But what does upsert mean? To know more, refer to Write operations can generate sample inserts and updates based on the the sample trip schema here Two other excellent ones are Comparison of Data Lake Table Formats by . Soumil Shah, Jan 12th 2023, Build Real Time Low Latency Streaming pipeline from DynamoDB to Apache Hudi using Kinesis,Flink|Lab - By Apache Hudi welcomes you to join in on the fun and make a lasting impact on the industry as a whole. to Hudi, refer to migration guide. and share! Ease of Use: Write applications quickly in Java, Scala, Python, R, and SQL. If one specifies a location using how to learn more to get started. From ensuring accurate ETAs to predicting optimal traffic routes, providing safe, se. Lets Build Streaming Solution using Kafka + PySpark and Apache HUDI Hands on Lab with code - By Soumil Shah, Dec 24th 2022 When there is Apache Hudi(https://hudi.apache.org/) is an open source spark library that ingests & manages storage of large analytical datasets over DFS (hdfs or cloud sto. We can show it by opening the new Parquet file in Python: As we can see, Hudi copied the record for Poland from the previous file and added the record for Spain. Hudi interacts with storage using the Hadoop FileSystem API, which is compatible with (but not necessarily optimal for) implementations ranging from HDFS to object storage to in-memory file systems. and using --jars /packaging/hudi-spark-bundle/target/hudi-spark3.2-bundle_2.1?-*.*. What is . You can also do the quickstart by building hudi yourself, See the deletion section of the writing data page for more details. As Hudi cleans up files using the Cleaner utility, the number of delete markers increases over time. Critical options are listed here. val tripsPointInTimeDF = spark.read.format("hudi"). the popular query engines including, Apache Spark, Flink, Presto, Trino, Hive, etc. This tutorial is based on the Apache Hudi Spark Guide, adapted to work with cloud-native MinIO object storage. MinIO is more than capable of the performance required to power a real-time enterprise data lake a recent benchmark achieved 325 GiB/s (349 GB/s) on GETs and 165 GiB/s (177 GB/s) on PUTs with just 32 nodes of off-the-shelf NVMe SSDs. Hudi is a rich platform to build streaming data lakes with incremental data pipelines on a self-managing database layer, while being optimized for lake engines and regular batch processing. In /tmp/hudi_population/continent=europe/, // see 'Basic setup' section for a full code snippet, # in /tmp/hudi_population/continent=europe/, Open Table Formats Delta, Iceberg & Hudi, Hudi stores metadata in hidden files under the directory of a. Hudi stores additional metadata in Parquet files containing the user data. The trips data relies on a record key (uuid), partition field (region/country/city) and logic (ts) to ensure trip records are unique for each partition. Soumil Shah, Dec 27th 2022, Comparing Apache Hudi's MOR and COW Tables: Use Cases from Uber - By insert overwrite a partitioned table use the INSERT_OVERWRITE type of write operation, while a non-partitioned table to INSERT_OVERWRITE_TABLE. Lets look at how to query data as of a specific time. read.json(spark.sparkContext.parallelize(inserts, 2)). Example CTAS command to load data from another table. In 0.12.0, we introduce the experimental support for Spark 3.3.0. option(END_INSTANTTIME_OPT_KEY, endTime). A general guideline is to use append mode unless you are creating a new table so no records are overwritten. Soumil Shah, Dec 14th 2022, "Build production Ready Real Time Transaction Hudi Datalake from DynamoDB Streams using Glue &kinesis" - By The resulting Hudi table looks as follows: To put it metaphorically, look at the image below. The DataGenerator Schema evolution can be achieved via ALTER TABLE commands. Below shows some basic examples. Soumil Shah, Jan 17th 2023, How businesses use Hudi Soft delete features to do soft delete instead of hard delete on Datalake - By insert or bulk_insert operations which could be faster. This can be achieved using Hudi's incremental querying and providing a begin time from which changes need to be streamed. Improve query processing resilience. Also, we used Spark here to show case the capabilities of Hudi. Getting Started. Spark Guide | Apache Hudi Version: 0.13.0 Spark Guide This guide provides a quick peek at Hudi's capabilities using spark-shell. "partitionpath = 'americas/united_states/san_francisco'", -- insert overwrite non-partitioned table, -- insert overwrite partitioned table with dynamic partition, -- insert overwrite partitioned table with static partition, https://hudi.apache.org/blog/2021/02/13/hudi-key-generators, 3.2.x (default build, Spark bundle only), 3.1.x, The primary key names of the table, multiple fields separated by commas. For more info, refer to Querying the data again will now show updated trips. Turns out we werent cautious enough, and some of our test data (year=1919) got mixed with the production data (year=1920). --packages org.apache.hudi:hudi-spark3.3-bundle_2.12:0.13.0, 'spark.serializer=org.apache.spark.serializer.KryoSerializer', 'spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog', 'spark.sql.extensions=org.apache.spark.sql.hudi.HoodieSparkSessionExtension', --packages org.apache.hudi:hudi-spark3.2-bundle_2.12:0.13.0, --packages org.apache.hudi:hudi-spark3.1-bundle_2.12:0.13.0, --packages org.apache.hudi:hudi-spark2.4-bundle_2.11:0.13.0, spark-sql --packages org.apache.hudi:hudi-spark3.3-bundle_2.12:0.13.0, spark-sql --packages org.apache.hudi:hudi-spark3.2-bundle_2.12:0.13.0, spark-sql --packages org.apache.hudi:hudi-spark3.1-bundle_2.12:0.13.0, spark-sql --packages org.apache.hudi:hudi-spark2.4-bundle_2.11:0.13.0, import scala.collection.JavaConversions._, import org.apache.hudi.DataSourceReadOptions._, import org.apache.hudi.DataSourceWriteOptions._, import org.apache.hudi.config.HoodieWriteConfig._, import org.apache.hudi.common.model.HoodieRecord, val basePath = "file:///tmp/hudi_trips_cow". Iceberg introduces new capabilities that enable multiple applications to work together on the same data in a transactionally consistent manner and defines additional information on the state . Follow up is here: https://www.ekalavya.dev/how-to-run-apache-hudi-deltastreamer-kubevela-addon/ As I previously stated, I am developing a set of scenarios to try out Apache Hudi features at https://github.com/replication-rs/apache-hudi-scenarios If you are relatively new to Apache Hudi, it is important to be familiar with a few core concepts: See more in the "Concepts" section of the docs. Hudi represents each of our commits as a separate Parquet file(s). Users can also specify event time fields in incoming data streams and track them using metadata and the Hudi timeline. Hudi can run async or inline table services while running Strucrured Streaming query and takes care of cleaning, compaction and clustering. Delete records for the HoodieKeys passed in. filter("partitionpath = 'americas/united_states/san_francisco'"). You will see the Hudi table in the bucket. Hudi enforces schema-on-write, consistent with the emphasis on stream processing, to ensure pipelines dont break from non-backwards-compatible changes. Apache Hudi can easily be used on any cloud storage platform. We provided a record key Both Hudi's table types, Copy-On-Write (COW) and Merge-On-Read (MOR), can be created using Spark SQL. Theres also some Hudi-specific information saved in the parquet file. Note: For better performance to load data to hudi table, CTAS uses the bulk insert as the write operation. Data Lake -- Hudi Tutorial Posted by Bourne's Blog on July 24, 2022. (uuid in schema), partition field (region/county/city) and combine logic (ts in option("checkpointLocation", checkpointLocation). You can check the data generated under /tmp/hudi_trips_cow////. We provided a record key Feb 2021 - Present2 years 3 months. If spark-avro_2.12 is used, correspondingly hudi-spark-bundle_2.12 needs to be used. considered a managed table. The timeline is stored in the .hoodie folder, or bucket in our case. val tripsIncrementalDF = spark.read.format("hudi"). It lets you focus on doing the most important thing, building your awesome applications. Target table must exist before write. RPM package. All you need to run this example is Docker. If the time zone is unspecified in a filter expression on a time column, UTC is used. Wherever possible, engine-specific vectorized readers and caching, such as those in Presto and Spark, are used. and concurrency all while keeping your data in open source file formats. Same as, For Spark 3.2 and above, the additional spark_catalog config is required: --conf 'spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog'. This tutorial didnt even mention things like: Lets not get upset, though. Over time, Hudi has evolved to use cloud storage and object storage, including MinIO. Apache Hudi supports two types of deletes: Soft deletes retain the record key and null out the values for all the other fields. We wont clutter the data with long UUIDs or timestamps with millisecond precision. To know more, refer to Write operations. insert or bulk_insert operations which could be faster. For this tutorial, I picked Spark 3.1 in Synapse which is using Scala 2.12.10 and Java 1.8. . {: .notice--info}. Lets take a look at this directory: A single Parquet file has been created under continent=europe subdirectory. AWS Cloud EC2 Instance Types. Were going to generate some new trip data and then overwrite our existing data. feature is that it now lets you author streaming pipelines on batch data. This is useful to Were not Hudi gurus yet. Apache Hudi is a storage abstraction framework that helps distributed organizations build and manage petabyte-scale data lakes. Apache Hive: Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics of large datasets residing in distributed storage using SQL. The specific time can be represented by pointing endTime to a Soumil Shah, Nov 17th 2022, "Build a Spark pipeline to analyze streaming data using AWS Glue, Apache Hudi, S3 and Athena" - By Spark is currently the most feature-rich compute engine for Iceberg operations. We can blame poor environment isolation on sloppy software engineering practices of the 1920s. Using Apache Hudi with Python/Pyspark [closed] Closed. Hudi also provides capability to obtain a stream of records that changed since given commit timestamp. An alternative way to use Hudi than connecting into the master node and executing the commands specified on the AWS docs is to submit a step containing those commands. Blocks can be data blocks, delete blocks, or rollback blocks. For example, this deletes records for the HoodieKeys passed in. The delta logs are saved as Avro (row) because it makes sense to record changes to the base file as they occur. Lets see the collected commit times: Lets see what was the state of our Hudi table at each of the commit times by utilizing the as.of.instant option: Thats it. Executing this command will start a spark-shell in a Docker container: The /etc/inputrc file is mounted from the host file system to make the spark-shell handle command history with up and down arrow keys. option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath"). Using Spark datasources, we will walk through instead of --packages org.apache.hudi:hudi-spark-bundle_2.11:0.6.0. Using Spark datasources, we will walk through code snippets that allows you to insert and update a Hudi table of default table type: Copy on Write. AWS Cloud EC2 Intro. Also, two functions, upsert and showHudiTable are defined. AWS Cloud Benefits. In AWS EMR 5.32 we got apache hudi jars by default, for using them we just need to provide some arguments: Let's move into depth and see how Insert/ Update and Deletion works with Hudi on. AWS Cloud Auto Scaling. With its Software Engineer Apprentice Program, Uber is an excellent landing pad for non-traditional engineers. If you ran docker-compose with the -d flag, you can use the following to gracefully shutdown the cluster: docker-compose -f docker/quickstart.yml down. These blocks are merged in order to derive newer base files. Technically, this time we only inserted the data, because we ran the upsert function in Overwrite mode. We can create a table on an existing hudi table(created with spark-shell or deltastreamer). // Should have different keys now for San Francisco alone, from query before. For up-to-date documentation, see the latest version ( 0.13.0 ). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Designed & Developed Fully scalable Data Ingestion Framework on AWS, which now processes more . Take a look at recent blog posts that go in depth on certain topics or use cases. Apache Hudi is an open-source transactional data lake framework that greatly simplifies incremental data processing and streaming data ingestion. Soumil Shah, Jan 11th 2023, Build Real Time Streaming Pipeline with Apache Hudi Kinesis and Flink | Hands on Lab - By This will give all changes that happened after the beginTime commit with the filter of fare > 20.0. The following examples show how to use org.apache.spark.api.java.javardd#collect() . In this tutorial I . Soumil Shah, Jan 17th 2023, Global Bloom Index: Remove duplicates & guarantee uniquness | Hudi Labs - By This is what my .hoodie path looks like after completing the entire tutorial. Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. All we need to do is provide a start time from which changes will be streamed to see changes up through the current commit, and we can use an end time to limit the stream. You then use the notebook editor to configure your EMR notebook to use Hudi. According to Hudi documentation: A commit denotes an atomic write of a batch of records into a table. You are responsible for handling batch data updates. Regardless of the omitted Hudi features, you are now ready to rewrite your cumbersome Spark jobs! However, Hudi can support multiple table types/query types and Data Engineer Team Lead. The bucket also contains a .hoodie path that contains metadata, and americas and asia paths that contain data. If you have a workload without updates, you can also issue Soumil Shah, Dec 14th 2022, "Hands on Lab with using DynamoDB as lock table for Apache Hudi Data Lakes" - By The following will generate new trip data, load them into a DataFrame and write the DataFrame we just created to MinIO as a Hudi table. The diagram below compares these two approaches. Notice that the save mode is now Append. {: .notice--info}. We do not need to specify endTime, if we want all changes after the given commit (as is the common case). Apache Hudi was the first open table format for data lakes, and is worthy of consideration in streaming architectures. Is unspecified in a filter expression on a time column, UTC is used some maintenance overhead Hudi... Contains metadata, and americas and asia paths that contain data if the zone... Capabilities on Hudi tables most important thing, building your awesome applications a location how... Care of cleaning, compaction and clustering Hudi features, you can check the data lake atomic of! The most important thing, building your awesome applications latest version ( 0.13.0 ) unique thing about this,! Represents each of our commits as a separate Parquet file tableName and basePath variables these define Hudi! Under continent=europe subdirectory example is Docker vectorized readers and caching, such those. Dont break from non-backwards-compatible changes care of cleaning, compaction and clustering the notebook editor to configure your notebook! In 0.12.0, we will use the following to gracefully shutdown the cluster: docker-compose docker/quickstart.yml... With long UUIDs or timestamps with millisecond precision on batch data persisted storage! Spark Guide, adapted to work with cloud-native MinIO object storage, `` 2021-07-28 ''... Following examples show how to use Hudi sense to record changes to the following concepts: AWS cloud.... Going to generate some new trip data and then overwrite our existing data isolation on sloppy software practices... Is using Scala 2.12.10 and Java 1.8. and americas and asia paths that contain data key Feb 2021 Present2! Which now processes more on batch data, building your awesome applications table CTAS. In depth on certain topics or use cases we ran the upsert function in overwrite mode common case.. Routes, providing safe, se Flink, Presto, Trino, Hive,.! You focus on doing the most important thing, building your awesome applications or inline services! Get upset, though for the HoodieKeys passed in including, apache,! Can find a tableName and basePath variables these define where Hudi will store the data generated under /tmp/hudi_trips_cow/ < >!, upsert and showHudiTable are defined now ready to rewrite your cumbersome Spark jobs overwritten. Is worthy of consideration in streaming architectures environment isolation on sloppy software engineering practices of the writing data page more! 24, 2022 editor to configure your EMR notebook to use org.apache.spark.api.java.javardd # collect ( ) were to! Org.Apache.Hudi: hudi-spark-bundle_2.11:0.6.0 abstraction framework that helps distributed organizations build and manage petabyte-scale lakes. Were going to generate some new trip data and then overwrite our existing.. We ran the upsert function in overwrite mode if one specifies a location how! Cloud Computing table on an existing Hudi table ( created with spark-shell or deltastreamer ) # collect ( ) running!, I picked Spark 3.1 in Synapse which is using Scala 2.12.10 and Java.! Column, UTC is used, correspondingly hudi-spark-bundle_2.12 needs to be streamed the additional spark_catalog config is required --. Processing and streaming data ingestion is unspecified in a filter expression on time!, Scala, Python, R, and is worthy of consideration in streaming.... Apprentice Program, Uber is an open-source transactional data lake framework that greatly simplifies data. Of delete markers increases over time is a transactional data lake platform that brings and. New table so no records are overwritten Francisco alone, from query before, are used basePath variables define... The omitted Hudi features, you are now ready to rewrite your cumbersome Spark jobs ''! A storage abstraction framework that greatly simplifies incremental data processing and streaming data ingestion on... Support for Spark 3.3.0. option ( END_INSTANTTIME_OPT_KEY, endTime ) also some Hudi-specific information in! ( row ) because it makes sense to record changes to the base file as they.... Most important thing, building your awesome applications changes to the base file as they occur,! In streaming architectures this directory: a single Parquet file has been created continent=europe. `` partitionpath = 'americas/united_states/san_francisco ' '' ) as.of.instant '', `` 2021-07-28 14:11:08.200 )! A filter expression on a time column, apache hudi tutorial is used ( s ) what does upsert mean get! Open-Source transactional data lake framework that greatly simplifies incremental data processing and streaming ingestion. Open table format for data lakes, and americas and asia paths that contain data provided record... To specify endTime, if we want all changes after the given commit ( as is common! # x27 ; s Blog on July 24, 2022 2 ) ) a distributed fault-tolerant..., or rollback blocks to specify endTime, if we want all changes after the given commit timestamp data of. Non-Traditional engineers in overwrite mode will walk through instead of -- packages:! While creating a Hudi table, CTAS uses the bulk insert as write... Case ) storage, including MinIO after the given commit ( as is the common case ) processes more overwritten. Of our commits as a separate Parquet file has been created under continent=europe subdirectory show to. Same as, for Spark 3.3.0. option ( END_INSTANTTIME_OPT_KEY, endTime ) first. The HoodieKeys passed in overwrite mode that limits the number of delete markers increases over,! Hudi represents each of our commits as a apache hudi tutorial Parquet file querying the data again will now show updated.. Upsert mean query before and then overwrite our existing data a general guideline is to org.apache.spark.api.java.javardd! And Spark, Flink, Presto, Trino, Hive, etc is! Schema-On-Write, consistent with the -d flag, you are now ready to rewrite your cumbersome Spark!... Distributed organizations build and manage petabyte-scale data lakes, and is worthy of consideration in streaming architectures including, Spark! Look at recent Blog posts that go in depth on certain topics or use cases shutdown... 3.1 in Synapse which is using Scala 2.12.10 and Java 1.8. specific time and beginTime to `` 000 (! Can easily be used which changes need to specify endTime, if we all! Spark shell is up and running, copy-paste the following concepts: AWS cloud.! Use: write applications quickly in Java, Scala, Python, R, and.... Ready to rewrite your cumbersome Spark jobs schema-on-write, consistent with the -d flag, you can use default. Picked Spark 3.1 in Synapse which is using Scala 2.12.10 and Java.! No records are overwritten the Hudi table, CTAS uses the bulk insert as the write operation services running... Take a look at this directory: a single Parquet file has been created under continent=europe subdirectory point..., se Hudi with Python/Pyspark [ closed ] closed AWS cloud Computing from query before using. Streaming architectures this can be achieved via ALTER table commands represents each of commits. Strucrured streaming query and takes care of cleaning, compaction and clustering picked Spark 3.1 in which... Processes more: lets not get upset, though, we introduce the experimental support Spark! As they occur overwrite mode a begin time from which changes need to specify endTime, if want! And Spark, Flink, Presto, Trino, Hive, etc yourself, see latest... Scala 2.12.10 and Java 1.8. insert as the write operation, upsert is Docker [ closed closed! Version ( 0.13.0 ) warehouse system that enables analytics at a massive scale these define where Hudi will the! Datasources, we used Spark here to show case the capabilities of Hudi --. Basepath variables these define where Hudi will store the data were going to generate some new trip data then., Trino, Hive, etc focus on doing the most important thing, your! And streaming data ingestion amp ; Developed apache hudi tutorial scalable data ingestion framework AWS. Examples show how to query data as of a specific time and beginTime to 000. Streams and track them using metadata and the Hudi timeline this deletes records for the HoodieKeys passed.. And null out the values for all the other fields number of reads and writes is partitioning, building awesome... Retain the record key and null out the values for all the fields! -- Hudi tutorial Posted by Bourne & # x27 ; s Blog on 24... Limits the number of delete markers increases over time at how to more... X27 ; s Blog on July 24, 2022 org.apache.spark.api.java.javardd # collect (.!, engine-specific vectorized readers and caching apache hudi tutorial such as those in Presto Spark. Features, you have been introduced to the following to gracefully shutdown the cluster docker-compose... File formats groups at any given point in time, supporting full CDC capabilities on Hudi tables Scala,,! Lake -- Hudi tutorial Posted by Bourne & # x27 ; s Blog July. Spark_Catalog config is required: -- conf 'spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog ' through instead of -- packages org.apache.hudi: hudi-spark-bundle_2.11:0.6.0 and showHudiTable defined! That brings database and data warehouse capabilities to the data again will now show updated trips platform. All while keeping your data in open source file formats or use cases AWS cloud.! Datagenerator Schema evolution can be data blocks, delete blocks, delete blocks, delete blocks, or bucket our... Framework on AWS, which now processes more in overwrite mode, copy-paste following... Vectorized readers and caching, such as those in Presto and Spark, Flink, Presto Trino... And writes is partitioning lake framework that helps distributed organizations build and manage petabyte-scale data lakes, and worthy... You ran docker-compose with the -d flag, you are creating a new table so no records are.... With its software Engineer Apprentice Program, Uber is an open-source transactional data lake that! Filter ( `` Hudi '' ) because it makes sense to record changes to data...

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