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commit 7abb7b4cd29bed9de908045139c0ae818202956f Author: Kyle Weller <[email protected]> AuthorDate: Tue Mar 5 00:20:32 2024 -0800 updated logos to have TM, updated all references of XTable to Apache XTableTM, removed unused images, and addressed all current comments (forgot to git add in prev commit :) ) --- README.md | 121 +++++++++++++++----------- website/README.md | 6 +- website/blog/OneTable-is-now-Apache-XTable.md | 12 +-- website/docs/athena.md | 6 +- website/docs/bigquery.md | 10 +-- website/docs/catalogs-index.md | 2 +- website/docs/demo/docker.md | 6 +- website/docs/fabric.md | 10 +-- website/docs/features-and-limitations.md | 4 +- website/docs/glue-catalog.md | 8 +- website/docs/hms.md | 2 +- website/docs/how-to.md | 8 +- website/docs/integrations-index.md | 2 +- website/docs/presto.md | 14 +-- website/docs/query-engines-index.md | 4 +- website/docs/redshift.md | 4 +- website/docs/setup.md | 2 +- website/docs/snowflake.md | 2 +- website/docs/spark.md | 2 +- website/docs/trino.md | 10 +-- website/static/images/xtable-svg.svg | 82 ++++++++--------- website/static/images/xtable-white-svg.svg | 68 ++++++--------- website/static/images/xtable-white.png | Bin 60873 -> 60672 bytes website/static/images/xtable-words-white.png | Bin 15321 -> 13975 bytes website/static/images/xtable-words.png | Bin 16972 -> 15930 bytes website/static/images/xtable.png | Bin 77607 -> 77700 bytes website/static/index.html | 52 +++++------ 27 files changed, 215 insertions(+), 222 deletions(-) diff --git a/README.md b/README.md index 6c9b0894..9769dce5 100644 --- a/README.md +++ b/README.md @@ -1,73 +1,81 @@ -# Apache XTable (Incubating) +# Apache XTable™ (Incubating) [](https://dev.azure.com/apache-xtable-ci-org/apache-xtable-ci/_build/latest?definitionId=2&branchName=main) -Apache XTable is a cross-table converter for table formats that facilitates omni-directional interoperability across data processing systems -and query engines. -Currently, XTable supports widely adopted open-source table formats such as Apache Hudi, Apache Iceberg, and Delta Lake. +Apache XTable™ (Incubating) is a cross-table converter for table formats that facilitates omni-directional interoperability across +data processing systems and query engines. Currently, Apache XTable™ supports widely adopted open-source table formats such as +Apache Hudi, Apache Iceberg, and Delta Lake. -XTable simplifies data lake operations by leveraging a common model for table representation. -This allows users to write data in one format while still benefiting from integrations and features available in other -formats. -For instance, XTable enables existing Hudi users to seamlessly work with Databricks's Photon Engine or query Iceberg -tables with Snowflake. -Creating transformations from one format to another is straightforward and only requires the implementation of a few interfaces, -which we believe will facilitate the expansion of supported source and target formats in the future. +Apache XTable™ simplifies data lake operations by leveraging a common model for table representation. This allows users to write +data in one format while still benefiting from integrations and features available in other formats. For instance, +Apache XTable™ enables existing Hudi users to seamlessly work with Databricks's Photon Engine or query Iceberg tables with +Snowflake. Creating transformations from one format to another is straightforward and only requires the implementation +of a few interfaces, which we believe will facilitate the expansion of supported source and target formats in the +future. # Building the project and running tests. -1. Use Java11 for building the project. If you are using some other java version, you can use [jenv](https://github.com/jenv/jenv) to use multiple java versions locally. + +1. Use Java11 for building the project. If you are using some other java version, you can + use [jenv](https://github.com/jenv/jenv) to use multiple java versions locally. 2. Build the project using `mvn clean package`. Use `mvn clean package -DskipTests` to skip tests while building. -3. Use `mvn clean test` or `mvn test` to run all unit tests. If you need to run only a specific test you can do this - by something like `mvn test -Dtest=TestDeltaSync -pl core`. +3. Use `mvn clean test` or `mvn test` to run all unit tests. If you need to run only a specific test you can do this by + something like `mvn test -Dtest=TestDeltaSync -pl core`. 4. Similarly, use `mvn clean verify` or `mvn verify` to run integration tests. # Style guide -1. We use [Maven Spotless plugin](https://github.com/diffplug/spotless/tree/main/plugin-maven) and + +1. We use [Maven Spotless plugin](https://github.com/diffplug/spotless/tree/main/plugin-maven) and [Google java format](https://github.com/google/google-java-format) for code style. -2. Use `mvn spotless:check` to find out code style violations and `mvn spotless:apply` to fix them. - Code style check is tied to compile phase by default, so code style violations will lead to build failures. +2. Use `mvn spotless:check` to find out code style violations and `mvn spotless:apply` to fix them. Code style check is + tied to compile phase by default, so code style violations will lead to build failures. # Running the bundled jar + 1. Get a pre-built bundled jar or create the jar with `mvn install -DskipTests` 2. create a yaml file that follows the format below: + ```yaml sourceFormat: HUDI targetFormats: - DELTA - ICEBERG datasets: - - - tableBasePath: s3://tpc-ds-datasets/1GB/hudi/call_center + - tableBasePath: s3://tpc-ds-datasets/1GB/hudi/call_center tableDataPath: s3://tpc-ds-datasets/1GB/hudi/call_center/data tableName: call_center namespace: my.db - - - tableBasePath: s3://tpc-ds-datasets/1GB/hudi/catalog_sales + - tableBasePath: s3://tpc-ds-datasets/1GB/hudi/catalog_sales tableName: catalog_sales partitionSpec: cs_sold_date_sk:VALUE - - - tableBasePath: s3://hudi/multi-partition-dataset + - tableBasePath: s3://hudi/multi-partition-dataset tableName: multi_partition_dataset partitionSpec: time_millis:DAY:yyyy-MM-dd,type:VALUE - - - tableBasePath: abfs://[email protected]/multi-partition-dataset + - tableBasePath: abfs://[email protected]/multi-partition-dataset tableName: multi_partition_dataset ``` + - `sourceFormat` is the format of the source table that you want to convert - `targetFormats` is a list of formats you want to create from your source tables - `tableBasePath` is the basePath of the table -- `tableDataPath` is an optional field specifying the path to the data files. If not specified, the tableBasePath will be used. For Iceberg source tables, you will need to specify the `/data` path. +- `tableDataPath` is an optional field specifying the path to the data files. If not specified, the tableBasePath will + be used. For Iceberg source tables, you will need to specify the `/data` path. - `namespace` is an optional field specifying the namespace of the table and will be used when syncing to a catalog. -- `partitionSpec` is a spec that allows us to infer partition values. This is only required for Hudi source tables. If the table is not partitioned, leave it blank. If it is partitioned, you can specify a spec with a comma separated list with format `path:type:format` - - `path` is a dot separated path to the partition field - - `type` describes how the partition value was generated from the column value - - `VALUE`: an identity transform of field value to partition value - - `YEAR`: data is partitioned by a field representing a date and year granularity is used - - `MONTH`: same as `YEAR` but with month granularity - - `DAY`: same as `YEAR` but with day granularity - - `HOUR`: same as `YEAR` but with hour granularity - - `format`: if your partition type is `YEAR`, `MONTH`, `DAY`, or `HOUR` specify the format for the date string as it appears in your file paths -3. The default implementations of table format clients can be replaced with custom implementations by specifying a client configs yaml file in the format below: +- `partitionSpec` is a spec that allows us to infer partition values. This is only required for Hudi source tables. If + the table is not partitioned, leave it blank. If it is partitioned, you can specify a spec with a comma separated list + with format `path:type:format` + - `path` is a dot separated path to the partition field + - `type` describes how the partition value was generated from the column value + - `VALUE`: an identity transform of field value to partition value + - `YEAR`: data is partitioned by a field representing a date and year granularity is used + - `MONTH`: same as `YEAR` but with month granularity + - `DAY`: same as `YEAR` but with day granularity + - `HOUR`: same as `YEAR` but with hour granularity + - `format`: if your partition type is `YEAR`, `MONTH`, `DAY`, or `HOUR` specify the format for the date string as it + appears in your file paths + +3. The default implementations of table format clients can be replaced with custom implementations by specifying a + client configs yaml file in the format below: + ```yaml # sourceClientProviderClass: The class name of a table format's client factory, where the client is # used for reading from a table of this format. All user configurations, including hadoop config @@ -77,15 +85,18 @@ datasets: # used for writing to a table of this format. # configuration: A map of configuration values specific to this client. tableFormatsClients: - HUDI: - sourceClientProviderClass: io.onetable.hudi.HudiSourceClientProvider - DELTA: - targetClientProviderClass: io.onetable.delta.DeltaClient - configuration: - spark.master: local[2] - spark.app.name: onetableclient + HUDI: + sourceClientProviderClass: io.onetable.hudi.HudiSourceClientProvider + DELTA: + targetClientProviderClass: io.onetable.delta.DeltaClient + configuration: + spark.master: local[2] + spark.app.name: onetableclient ``` -4. A catalog can be used when reading and updating Iceberg tables. The catalog can be specified in a yaml file and passed in with the `--icebergCatalogConfig` option. The format of the catalog config file is: + +4. A catalog can be used when reading and updating Iceberg tables. The catalog can be specified in a yaml file and + passed in with the `--icebergCatalogConfig` option. The format of the catalog config file is: + ```yaml catalogImpl: io.my.CatalogImpl catalogName: name @@ -93,20 +104,30 @@ catalogOptions: # all other options are passed through in a map key1: value1 key2: value2 ``` -5. run with `java -jar utilities/target/utilities-0.1.0-SNAPSHOT-bundled.jar --datasetConfig my_config.yaml [--hadoopConfig hdfs-site.xml] [--clientsConfig clients.yaml] [--icebergCatalogConfig catalog.yaml]` -The bundled jar includes hadoop dependencies for AWS, Azure, and GCP. Authentication for AWS is done with -`com.amazonaws.auth.DefaultAWSCredentialsProviderChain`. To override this setting, specify a different implementation -with the `--awsCredentialsProvider` option. + +5. run + with `java -jar utilities/target/utilities-0.1.0-SNAPSHOT-bundled.jar --datasetConfig my_config.yaml [--hadoopConfig hdfs-site.xml] [--clientsConfig clients.yaml] [--icebergCatalogConfig catalog.yaml]` + The bundled jar includes hadoop dependencies for AWS, Azure, and GCP. Authentication for AWS is done with + `com.amazonaws.auth.DefaultAWSCredentialsProviderChain`. To override this setting, specify a different implementation + with the `--awsCredentialsProvider` option. # Contributing + ## Setup + For setting up the repo on IntelliJ, open the project and change the java version to Java11 in File->ProjectStructure  -You have found a bug, or have a cool idea you that want to contribute to the project ? Please file a GitHub issue [here](https://github.com/onetable-io/onetable/issues) +You have found a bug, or have a cool idea you that want to contribute to the project ? Please file a GitHub +issue [here](https://github.com/onetable-io/onetable/issues) ## Adding a new target format -Adding a new target format requires a developer implement [TargetClient](./api/src/main/java/io/onetable/spi/sync/TargetClient.java). Once you have implemented that interface, you can integrate it into the [OneTableClient](./core/src/main/java/io/onetable/client/OneTableClient.java). If you think others may find that target useful, please raise a Pull Request to add it to the project. + +Adding a new target format requires a developer +implement [TargetClient](./api/src/main/java/io/onetable/spi/sync/TargetClient.java). Once you have implemented that +interface, you can integrate it into the [OneTableClient](./core/src/main/java/io/onetable/client/OneTableClient.java). +If you think others may find that target useful, please raise a Pull Request to add it to the project. ## Overview of the sync process +  diff --git a/website/README.md b/website/README.md index 847e989d..74803fb6 100644 --- a/website/README.md +++ b/website/README.md @@ -1,6 +1,6 @@ -# Apache XTable (Incubating) Website Source Code +# Apache XTable™ (Incubating) Website Source Code -This repo hosts the source code of [XTable](https://github.com/apache/incubator-xtable) +This repo hosts the source code of [Apache XTable™](https://github.com/apache/incubator-xtable) ## Prerequisite @@ -63,4 +63,4 @@ npm run serve ## Maintainers -[XTable Community](https://incubator.apache.org/projects/xtable.html) +[Apache XTable™ Community](https://incubator.apache.org/projects/xtable.html) diff --git a/website/blog/OneTable-is-now-Apache-XTable.md b/website/blog/OneTable-is-now-Apache-XTable.md index 6a148a51..b7c768d3 100644 --- a/website/blog/OneTable-is-now-Apache-XTable.md +++ b/website/blog/OneTable-is-now-Apache-XTable.md @@ -1,5 +1,5 @@ --- -title: "OneTable is now “Apache XTable (Incubating)”" +title: "OneTable is now “Apache XTable™ (Incubating)”" excerpt: "XTable is now Incubating in the Apache Software Foundation" author: Dipankar Mazumdar, JB Onofré category: blog @@ -9,7 +9,7 @@ tags: - community --- -# OneTable is now “Apache XTable (Incubating)” +# OneTable is now “Apache XTable™ (Incubating)” Data Lakehouse table formats such as [Apache Hudi](https://hudi.apache.org/), [Delta Lake](https://delta.io/), and [Apache Iceberg](https://iceberg.apache.org/) have enabled users to establish open foundations for their data architecture. @@ -25,7 +25,7 @@ rewrite or duplicate the actual data files. The goal of OneTable was to anchor its success in neutrality, guided by strong community values. From the very beginning, the project[ expressed its desire](https://cwiki.apache.org/confluence/display/INCUBATOR/XTable+Proposal) to be incubated under the [Apache Software Foundation](https://www.apache.org/). Today, we are excited to announce that OneTable has been -accepted as an incubating project by Apache and will henceforth be known as **Apache XTable**. As part of the incubation, +accepted as an incubating project by Apache and will henceforth be known as **Apache XTable™**. As part of the incubation, the project has transitioned the code repository to the Apache infrastructure and adopted the [community-driven development principles](https://community.apache.org/) of the Apache Foundation. @@ -53,7 +53,7 @@ the public good. <img src="/images/blog/XTable/xtable-docs.png" alt="drawing" style={{width:'80%', display:'block', marginLeft:'auto', marginRight:'auto', marginTop:'18pt', marginBottom:'18pt'}} /> -For those interested in exploring Apache XTable, the official website is a good starting point. The documentation +For those interested in exploring Apache XTable™, the official website is a good starting point. The documentation section hosts a great hands-on [quickstart](https://onetable.dev/docs/how-to) guide to getting acquainted with XTable, providing a straightforward way to experience its interoperability capabilities firsthand. If you have specific ideas, questions, or seek direct interaction, the [discussions](https://github.com/onetable-io/onetable/discussions) section @@ -61,7 +61,7 @@ is available for more in-depth exchanges. We invite you to contribute to the pro filling issues to the [XTable GitHub repository](https://github.com/apache/incubator-xtable). Contributions in the early phase of the project are going to be critical as we build XTable together. Joining the [XTable mailing list](mailto:[email protected]) is another excellent way to stay informed and engage with -the project. We are really excited about the future of Apache XTable and building it together with the vibrant data community. +the project. We are really excited about the future of Apache XTable™ and building it together with the vibrant data community. -Follow Apache XTable on [LinkedIn](https://www.linkedin.com/company/apache-xtable/) and [Twitter](https://twitter.com/apachextable) +Follow Apache XTable™ on [LinkedIn](https://www.linkedin.com/company/apache-xtable/) and [Twitter](https://twitter.com/apachextable) to keep up with the latest updates! diff --git a/website/docs/athena.md b/website/docs/athena.md index 769cd460..7d2969d4 100644 --- a/website/docs/athena.md +++ b/website/docs/athena.md @@ -4,7 +4,7 @@ title: "Amazon Athena" --- # Querying from Amazon Athena -To read a XTable synced target table (regardless of the table format) in Amazon Athena, +To read a Apache XTable™ synced target table (regardless of the table format) in Amazon Athena, you can create the table either by: * Using a DDL statement as mentioned in the following AWS docs: * [Example](https://docs.aws.amazon.com/athena/latest/ug/querying-hudi.html#querying-hudi-in-athena-creating-hudi-tables) for Hudi @@ -12,8 +12,8 @@ you can create the table either by: * [Example](https://docs.aws.amazon.com/athena/latest/ug/querying-iceberg-creating-tables.html#querying-iceberg-creating-tables-query-editor) for Iceberg * Or maintain the tables in Glue Data Catalog -For an end to end tutorial that walks through S3, Glue Data Catalog and Athena to query a XTable synced table, -you can refer to the XTable [Glue Data Catalog Guide](/docs/glue-catalog). +For an end to end tutorial that walks through S3, Glue Data Catalog and Athena to query an Apache XTable™ synced table, +you can refer to the Apache XTable™ [Glue Data Catalog Guide](/docs/glue-catalog). :::danger LIMITATION for Hudi target format: To validate the Hudi targetFormat table results, you need to ensure that the query engine that you're using diff --git a/website/docs/bigquery.md b/website/docs/bigquery.md index d3f64cc4..73a8e3b1 100644 --- a/website/docs/bigquery.md +++ b/website/docs/bigquery.md @@ -6,11 +6,11 @@ title: "Google BigQuery" # Querying from Google BigQuery ### Iceberg tables -To read a XTable synced [Iceberg table from BigQuery](https://cloud.google.com/bigquery/docs/iceberg-tables), +To read an Apache XTable™ (Incubating) synced [Iceberg table from BigQuery](https://cloud.google.com/bigquery/docs/iceberg-tables), you have two options: #### [Using Iceberg JSON metadata file to create the Iceberg BigLake tables](https://cloud.google.com/bigquery/docs/iceberg-tables#create-using-metadata-file): -XTable outputs metadata files for Iceberg target format syncs which can be used by BigQuery +Apache XTable™ outputs metadata files for Iceberg target format syncs which can be used by BigQuery to read the BigLake tables. ```sql md title="sql" @@ -48,9 +48,9 @@ If you are not planning on using Iceberg, then you do not need to add these to y #### [Using BigLake Metastore to create the Iceberg BigLake tables](https://cloud.google.com/bigquery/docs/iceberg-tables#create-using-biglake-metastore): -You can use two options to register XTable synced Iceberg tables to BigLake Metastore: -* To directly register the XTable synced Iceberg table to BigLake Metastore, - follow the [XTable guide to integrate with BigLake Metastore](/docs/biglake-metastore) +You can use two options to register Apache XTable™ synced Iceberg tables to BigLake Metastore: +* To directly register the Apache XTable™ synced Iceberg table to BigLake Metastore, + follow the [Apache XTable™ guide to integrate with BigLake Metastore](/docs/biglake-metastore) * Use [stored procedures for Spark](https://cloud.google.com/bigquery/docs/spark-procedures) on BigQuery to register the table in BigLake Metastore and query the tables from BigQuery. diff --git a/website/docs/catalogs-index.md b/website/docs/catalogs-index.md index 4a9e212a..0a1da307 100644 --- a/website/docs/catalogs-index.md +++ b/website/docs/catalogs-index.md @@ -1,6 +1,6 @@ # Catalogs -Integrating the XTable synced target tables to external catalogs requires an explicit registration. This guide will +Integrating the Apache XTable™ (Incubating) synced target tables to external catalogs requires an explicit registration. This guide will walk you through the required steps. In addition to the information in this guide, we recommend you following official guidelines from the respective diff --git a/website/docs/demo/docker.md b/website/docs/demo/docker.md index c28c4c3f..93f63e33 100644 --- a/website/docs/demo/docker.md +++ b/website/docs/demo/docker.md @@ -3,14 +3,14 @@ sidebar_position: 1 title: "Docker Demo" --- -# Building interoperable tables using XTable -This demo walks you through a fictional use case and the steps to add interoperability between table formats using XTable. +# Building interoperable tables using Apache XTable™ (Incubating) +This demo walks you through a fictional use case and the steps to add interoperability between table formats using Apache XTable™. For this purpose, a self-contained data infrastructure is brought up as Docker containers within your computer. ## Pre-requisites * Install Docker in your local machine -* Clone [XTable GitHub repository](https://github.com/apache/incubator-xtable) +* Clone [Apache XTable™ GitHub repository](https://github.com/apache/incubator-xtable) :::note NOTE: This demo was tested in both x86-64 and AArch64 based macOS operating systems diff --git a/website/docs/fabric.md b/website/docs/fabric.md index 0d3f671c..f9ceee6f 100644 --- a/website/docs/fabric.md +++ b/website/docs/fabric.md @@ -8,8 +8,8 @@ import TabItem from '@theme/TabItem'; # Querying from Microsoft Fabric This guide offers a short tutorial on how to query Apache Iceberg and Apache Hudi tables in Microsoft Fabric utilizing -the translation capabilities of XTable. This tutorial is intended solely for demonstration and to verify the -compatibility of XTable's output with Fabric. The tutorial leverages the currently[^1] available features in Fabric, like +the translation capabilities of Apache XTable™ (Incubating). This tutorial is intended solely for demonstration and to verify the +compatibility of Apache XTable™'s output with Fabric. The tutorial leverages the currently[^1] available features in Fabric, like `Shortcuts`. @@ -26,7 +26,7 @@ to data in other file systems. ## Tutorial The objective of the following tutorial is to translate an Iceberg or Hudi table in ADLS storage account into Delta Lake -format using XTable. After translation, this table will be accessible for querying from various Fabric engines, +format using Apache XTable™. After translation, this table will be accessible for querying from various Fabric engines, including T-SQL, Spark, and Power BI. ### Pre-requisites @@ -51,8 +51,8 @@ spark.hadoop.fs.azure.account.oauth2.client.id=<client-id> spark.hadoop.fs.azure.account.oauth2.client.secret=<client-secret> ``` -### Step 2. Translate source table to Delta Lake format using XTable -This step translates the table `people` originally in Iceberg or Hudi format to Delta Lake format using XTable. +### Step 2. Translate source table to Delta Lake format using Apache XTable™ +This step translates the table `people` originally in Iceberg or Hudi format to Delta Lake format using Apache XTable™. The primary actions for the translation are documented in [Creating your first interoperable table - Running Sync](/docs/how-to#running-sync) tutorial section. However, since the table is in ADLS, you need to update datasets path and hadoop configurations. diff --git a/website/docs/features-and-limitations.md b/website/docs/features-and-limitations.md index c94ffabb..b5a5c8cb 100644 --- a/website/docs/features-and-limitations.md +++ b/website/docs/features-and-limitations.md @@ -8,9 +8,9 @@ import TabItem from '@theme/TabItem'; # Features and Limitations ## Features -XTable provides users with the ability to translate metadata from one table format to another. +Apache XTable™ (Incubating) provides users with the ability to translate metadata from one table format to another. -XTable provides two sync modes, "incremental" and "full." The incremental mode is more lightweight and has better performance, especially on large tables. If there is anything that prevents the incremental mode from working properly, the tool will fall back to the full sync mode. +Apache XTable™ provides two sync modes, "incremental" and "full." The incremental mode is more lightweight and has better performance, especially on large tables. If there is anything that prevents the incremental mode from working properly, the tool will fall back to the full sync mode. This sync provides users with the following: 1. Syncing of data files along with their column level statistics and partition metadata diff --git a/website/docs/glue-catalog.md b/website/docs/glue-catalog.md index 9175de51..ebc6e687 100644 --- a/website/docs/glue-catalog.md +++ b/website/docs/glue-catalog.md @@ -7,7 +7,7 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # Syncing to Glue Data Catalog -This document walks through the steps to register a XTable synced table in Glue Data Catalog on AWS. +This document walks through the steps to register an Apache XTable™ (Incubating) synced table in Glue Data Catalog on AWS. ## Pre-requisites 1. Source table(s) (Hudi/Delta/Iceberg) already written to Amazon S3. @@ -18,12 +18,12 @@ This document walks through the steps to register a XTable synced table in Glue [AWS docs](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) and also set up access credentials by following the steps [here](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-quickstart.html) -3. Clone the XTable [repository](https://github.com/apache/incubator-xtable) and create the +3. Clone the Apache XTable™ [repository](https://github.com/apache/incubator-xtable) and create the `utilities-0.1.0-SNAPSHOT-bundled.jar` by following the steps on the [Installation page](/docs/setup) ## Steps ### Running sync -Create `my_config.yaml` in the cloned XTable directory. +Create `my_config.yaml` in the cloned Apache XTable™ directory. <Tabs groupId="table-format" @@ -193,6 +193,6 @@ SELECT * FROM onetable_synced_db.<table_name>; ## Conclusion In this guide we saw how to, -1. sync a source table to create metadata for the desired target table formats using XTable +1. sync a source table to create metadata for the desired target table formats using Apache XTable™ 2. catalog the data in the target table format in Glue Data Catalog 3. query the target table using Amazon Athena diff --git a/website/docs/hms.md b/website/docs/hms.md index 18c6c7ea..4d7b71f8 100644 --- a/website/docs/hms.md +++ b/website/docs/hms.md @@ -7,7 +7,7 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # Syncing to Hive Metastore -This document walks through the steps to register a XTable synced table on Hive Metastore (HMS). +This document walks through the steps to register an Apache XTable™ (Incubating) synced table on Hive Metastore (HMS). ## Pre-requisites 1. Source table(s) (Hudi/Delta/Iceberg) already written to your local storage or external storage locations like S3/GCS/ADLS. diff --git a/website/docs/how-to.md b/website/docs/how-to.md index 8b0d5c7b..22f3af7c 100644 --- a/website/docs/how-to.md +++ b/website/docs/how-to.md @@ -9,13 +9,13 @@ import TabItem from '@theme/TabItem'; # Creating your first interoperable table :::danger Important -Using XTable to sync your source tables in different target format involves running sync on your +Using Apache XTable™ (Incubating) to sync your source tables in different target format involves running sync on your current dataset using a bundled jar. You can create this bundled jar by following the instructions -on the [Installation page](/docs/setup). Read through XTable's +on the [Installation page](/docs/setup). Read through Apache XTable™'s [GitHub page](https://github.com/apache/incubator-xtable#building-the-project-and-running-tests) for more information. ::: -In this tutorial we will look at how to use XTable to add interoperability between table formats. +In this tutorial we will look at how to use Apache XTable™ to add interoperability between table formats. For example, you can expose a table ingested with Hudi as an Iceberg and/or Delta Lake table without copying or moving the underlying data files used for that table while maintaining a similar commit history to enable proper point in time queries. @@ -23,7 +23,7 @@ history to enable proper point in time queries. ## Pre-requisites 1. A compute instance where you can run Apache Spark. This can be your local machine, docker, or a distributed service like Amazon EMR, Google Cloud's Dataproc, Azure HDInsight etc -2. Clone the XTable [repository](https://github.com/apache/incubator-xtable) and create the +2. Clone the Apache XTable™ [repository](https://github.com/apache/incubator-xtable) and create the `utilities-0.1.0-SNAPSHOT-bundled.jar` by following the steps on the [Installation page](/docs/setup) 3. Optional: Setup access to write to and/or read from distributed storage services like: * Amazon S3 by following the steps diff --git a/website/docs/integrations-index.md b/website/docs/integrations-index.md index cc4ddb64..b8b429a0 100644 --- a/website/docs/integrations-index.md +++ b/website/docs/integrations-index.md @@ -1,6 +1,6 @@ # Integrations -This guide will serve as a reference to integrate XTable synced tables to various +This guide will serve as a reference to integrate Apache XTable™ (Incubating) synced tables to various catalogs and query engines. In addition to the information in this guide, we recommend you following official guidelines diff --git a/website/docs/presto.md b/website/docs/presto.md index 2fee5352..6f49da84 100644 --- a/website/docs/presto.md +++ b/website/docs/presto.md @@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem'; # Querying from Presto Presto allows you to query table formats like Hudi, Delta and Iceberg using connectors. The same setup will -work for XTable synced tables as well. +work for Apache XTable™ (Incubating) synced tables as well. For more information and required configurations refer to: * [Hudi Connector](https://prestodb.io/docs/current/connector/hudi.html) @@ -19,12 +19,12 @@ For more information and required configurations refer to: :::danger Delta Lake: Delta Lake supports [generated columns](https://docs.databricks.com/en/delta/generated-columns.html) which are a special type of column whose values are automatically generated based on a user-specified function -over other columns in the Delta table. During sync, XTable uses the same logic to generate partition columns wherever required. -Currently, the generated columns from XTable sync shows `NULL` when queried from Presto CLI. +over other columns in the Delta table. During sync, Apache XTable™ uses the same logic to generate partition columns wherever required. +Currently, the generated columns from Apache XTable™ sync shows `NULL` when queried from Presto CLI. ::: For hands on experimentation, please follow [Creating your first interoperable table](/docs/how-to) tutorial -to create XTable synced tables followed by [Hive Metastore](/docs/hms) tutorial to register the target table +to create Apache XTable™ synced tables followed by [Hive Metastore](/docs/hms) tutorial to register the target table in Hive Metastore. Once done, follow the below high level steps: 1. If you are working with a self-managed Presto service, from the presto-server directory run `./bin/launcher run` 2. From the directory where you have installed presto-cli: login to presto-cli by running `./presto-cli` @@ -42,7 +42,7 @@ values={[ <TabItem value="hudi"> :::tip Note: -If you are following the example from [Hive Metastore](/docs/hms), you can query the XTable synced Hudi table +If you are following the example from [Hive Metastore](/docs/hms), you can query the Apache XTable™ synced Hudi table from Presto using the below query. ```sql md title="sql" SELECT * FROM hudi.hudi_db.<table_name>; @@ -53,7 +53,7 @@ SELECT * FROM hudi.hudi_db.<table_name>; <TabItem value="delta"> :::tip Note: -If you are following the example from [Hive Metastore](/docs/hms), you can query the XTable synced Delta table +If you are following the example from [Hive Metastore](/docs/hms), you can query the Apache XTable™ synced Delta table from Presto using the below query. ```sql md title="sql" SELECT * FROM delta.delta_db.<table_name>; @@ -64,7 +64,7 @@ SELECT * FROM delta.delta_db.<table_name>; <TabItem value="iceberg"> :::tip Note: -If you are following the example from [Hive Metastore](/docs/hms), you can query the XTable synced Iceberg table +If you are following the example from [Hive Metastore](/docs/hms), you can query the Apache XTable™ synced Iceberg table from Presto using the below query. ```sql md title="sql" SELECT * FROM iceberg.iceberg_db.<table_name>; diff --git a/website/docs/query-engines-index.md b/website/docs/query-engines-index.md index 46266259..f852c523 100644 --- a/website/docs/query-engines-index.md +++ b/website/docs/query-engines-index.md @@ -1,7 +1,7 @@ # Query Engines -XTable synced tables behave the similarly to native tables which means you do not need any additional configurations -on query engines' side to work with tables synced by XTable. This guide will delve into the details of working +Apache XTable™ (Incubating) synced tables behave the similarly to native tables which means you do not need any additional configurations +on query engines' side to work with tables synced by Apache XTable™. This guide will delve into the details of working with various query engines. For more information on how to sync a source format table to create necessary log files to be inferred as a different format table, refer to [Creating your first interoperable table guide](/docs/how-to) diff --git a/website/docs/redshift.md b/website/docs/redshift.md index 17d1ec73..64c2f4b4 100644 --- a/website/docs/redshift.md +++ b/website/docs/redshift.md @@ -4,7 +4,7 @@ title: "Amazon Redshift Spectrum" --- # Querying from Redshift Spectrum -To read a XTable synced target table (regardless of the table format) in Amazon Redshift, +To read a Apache XTable™ synced target table (regardless of the table format) in Amazon Redshift, users have to create an external schema and refer to the external data catalog that contains the table. Redshift infers the table's schema and format from the external catalog/database directly. For more information on creating external schemas, refer to @@ -41,7 +41,7 @@ You have two options to create and query Delta tables in Redshift Spectrum: 1. Follow the steps in [this](https://docs.delta.io/latest/redshift-spectrum-integration.html#set-up-a-redshift-spectrum-to-delta-lake-integration-and-query-delta-tables) article to set up a Redshift Spectrum to Delta Lake integration and query Delta tables directly from Amazon S3. -2. While creating the Glue Crawler to crawl the XTable synced Delta table, choose the `Create Symlink tables` +2. While creating the Glue Crawler to crawl the Apache XTable™ synced Delta table, choose the `Create Symlink tables` option in `Add data source` pop-up window. This will add `_symlink_format_manifest` folder with manifest files in the table root path. diff --git a/website/docs/setup.md b/website/docs/setup.md index 029d786a..0b6d70fa 100644 --- a/website/docs/setup.md +++ b/website/docs/setup.md @@ -1,6 +1,6 @@ # Installation -This page covers the essential steps to setup Apache XTable (incubating) in your environment. +This page covers the essential steps to setup Apache XTable™ (incubating) in your environment. ## Pre-requisites 1. Building the project requires Java 11 and Maven to be setup and configured using PATH or environment variables. diff --git a/website/docs/snowflake.md b/website/docs/snowflake.md index 83d87a11..882f8996 100644 --- a/website/docs/snowflake.md +++ b/website/docs/snowflake.md @@ -14,7 +14,7 @@ Iceberg on Snowflake is currently supported in ::: ## Steps: -These are high level steps to help you integrate XTable synced Iceberg tables on Snowflake. For more additional information +These are high level steps to help you integrate Apache XTable™ (Incubating) synced Iceberg tables on Snowflake. For more additional information refer to the [Getting started with Iceberg tables](https://docs.snowflake.com/LIMITEDACCESS/iceberg-2023/tables-iceberg-getting-started). ### Create an external volume diff --git a/website/docs/spark.md b/website/docs/spark.md index 876c0300..78945fca 100644 --- a/website/docs/spark.md +++ b/website/docs/spark.md @@ -7,7 +7,7 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # Querying from Apache Spark -To read a XTable synced target table (regardless of the table format) in Apache Spark locally or on services like +To read a Apache XTable™ (Incubating) synced target table (regardless of the table format) in Apache Spark locally or on services like Amazon EMR, Google Cloud's Dataproc, Azure HDInsight, or Databricks, you do not need additional jars or configs other than what is needed by the respective table formats. diff --git a/website/docs/trino.md b/website/docs/trino.md index 5ec91169..b870b713 100644 --- a/website/docs/trino.md +++ b/website/docs/trino.md @@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem'; # Querying from Trino Trino just like Presto allows you to query table formats like Hudi, Delta and Iceberg tables using connectors. -Users do not need additional configurations to work with XTable synced tables. +Users do not need additional configurations to work with Apache XTable™ (Incubating) synced tables. For more information and required configurations refer to: * [Hudi Connector](https://trino.io/docs/current/connector/hudi.html) @@ -17,7 +17,7 @@ For more information and required configurations refer to: * [Iceberg Connector](https://trino.io/docs/current/connector/iceberg.html) For hands on experimentation, please follow [Creating your first interoperable table](/docs/how-to#create-dataset) -to create XTable synced tables followed by [Hive Metastore](/docs/hms) to register the target table +to create Apache XTable™ synced tables followed by [Hive Metastore](/docs/hms) to register the target table in Hive Metastore. Once done, please follow the below high level steps: 1. Start the Trino server manually if you are working with a non-managed Trino service: from the trino-server directory run `./bin/launcher run` @@ -36,7 +36,7 @@ values={[ <TabItem value="hudi"> :::tip Note: -If you are following the example from [Hive Metastore](/docs/hms), you can query the XTable synced Hudi table +If you are following the example from [Hive Metastore](/docs/hms), you can query the Apache XTable™ synced Hudi table from Trino using the below query. ```sql md title="sql" SELECT * FROM hudi.hudi_db.<table_name>; @@ -47,7 +47,7 @@ SELECT * FROM hudi.hudi_db.<table_name>; <TabItem value="delta"> :::tip Note: -If you are following the example from [Hive Metastore](/docs/hms), you can query the XTable synced Delta table +If you are following the example from [Hive Metastore](/docs/hms), you can query the Apache XTable™ synced Delta table from Trino using the below query. ```sql md title="sql" SELECT * FROM delta.delta_db.<table_name>; @@ -58,7 +58,7 @@ SELECT * FROM delta.delta_db.<table_name>; <TabItem value="iceberg"> :::tip Note: -If you are following the example from [Hive Metastore](/docs/hms), you can query the XTable synced Iceberg table +If you are following the example from [Hive Metastore](/docs/hms), you can query the Apache XTable™ synced Iceberg table from Trino using the below query. ```sql md title="sql" SELECT * FROM iceberg.iceberg_db.<table_name>; diff --git a/website/static/images/xtable-svg.svg b/website/static/images/xtable-svg.svg index 6e310632..73ffcded 100644 --- a/website/static/images/xtable-svg.svg +++ b/website/static/images/xtable-svg.svg @@ -1,86 +1,72 @@ -<!--Licensed to the Apache Software Foundation (ASF) under one -or more contributor license agreements. See the NOTICE file -distributed with this work for additional information -regarding copyright ownership. The ASF licenses this file -to you under the Apache License, Version 2.0 (the -"License"); you may not use this file except in compliance -with the License. You may obtain a copy of the License at - -http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, -software distributed under the License is distributed on an -"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -KIND, either express or implied. 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100644 Binary files a/website/static/images/xtable-words.png and b/website/static/images/xtable-words.png differ diff --git a/website/static/images/xtable.png b/website/static/images/xtable.png index 43554889..afb6984a 100644 Binary files a/website/static/images/xtable.png and b/website/static/images/xtable.png differ diff --git a/website/static/index.html b/website/static/index.html index c9fa1f21..b2407217 100644 --- a/website/static/index.html +++ b/website/static/index.html @@ -3,13 +3,13 @@ <html data-wf-page="65402b66d39d6454e51fabf1" data-wf-site="65402b66d39d6454e51fabed"> <head> <meta charset="utf-8"> - <title>XTable</title> - <meta content="XTable is a cross-table interop of lakehouse table formats Apache Hudi, Apache Iceberg, and Delta Lake. XTable is NOT a new or separate format, XTable provides abstractions and tools for the translation of lakehouse table format metadata." name="description"> - <meta content="XTable" property="og:title"> - <meta content="XTable is a cross-table interop of lakehouse table formats Apache Hudi, Apache Iceberg, and Delta Lake. XTable is NOT a new or separate format, XTable provides abstractions and tools for the translation of lakehouse table format metadata." property="og:description"> + <title>Apache XTable™</title> + <meta content="Apache XTable™ is a cross-table interop of lakehouse table formats Apache Hudi, Apache Iceberg, and Delta Lake. Apache XTable™ is NOT a new or separate format, Apache XTable™ provides abstractions and tools for the translation of lakehouse table format metadata." name="description"> + <meta content="Apache XTable™" property="og:title"> + <meta content="Apache XTable™ is a cross-table interop of lakehouse table formats Apache Hudi, Apache Iceberg, and Delta Lake. Apache XTable™ is NOT a new or separate format, Apache XTable™ provides abstractions and tools for the translation of lakehouse table format metadata." property="og:description"> <meta content="https://uploads-ssl.webflow.com/65402b66d39d6454e51fabed/654c643bf9cebe95d6c3dd50_Group%201562%20(1).png" property="og:image"> - <meta content="XTable" property="twitter:title"> - <meta content="XTable is a cross-table interop of lakehouse table formats Apache Hudi, Apache Iceberg, and Delta Lake. XTable is NOT a new or separate format, XTable provides abstractions and tools for the translation of lakehouse table format metadata." property="twitter:description"> + <meta content="Apache XTable™" property="twitter:title"> + <meta content="Apache XTable™ is a cross-table interop of lakehouse table formats Apache Hudi, Apache Iceberg, and Delta Lake. Apache XTable™ is NOT a new or separate format, Apache XTable™ provides abstractions and tools for the translation of lakehouse table format metadata." property="twitter:description"> <meta content="https://uploads-ssl.webflow.com/65402b66d39d6454e51fabed/654c643bf9cebe95d6c3dd50_Group%201562%20(1).png" property="twitter:image"> <meta property="og:type" content="website"> <meta content="summary_large_image" name="twitter:card"> @@ -74,7 +74,7 @@ </section> <section class="callout-section"> <div class="w-layout-blockcontainer container-main w-container"> - Apache XTable is incubating in the Apache Software Foundation and was recently renamed from OneTable + Apache XTable™ is incubating in the Apache Software Foundation and was recently renamed from OneTable </div> </section> <section class="logo-section"> @@ -90,11 +90,11 @@ <section class="what-section"> <div class="w-layout-blockcontainer main-container-why w-container"> <div class="up-section-why"> - <h1 class="heading-section why">What is<span class="text-span"> XTable?</span></h1> + <h1 class="heading-section why">What is<span class="text-span"><br /> Apache XTable™?</span></h1> <ul role="list" class="list"> - <li class="list-item why">XTable provides cross-table omni-directional interop between lakehouse table formats</li> - <li class="list-item why">XTable is NOT a new or separate format, XTable provides abstractions and tools for the translation of lakehouse table format metadata</li> - <li class="list-item why">XTable is formerly known as OneTable</li> + <li class="list-item why">Apache XTable™ provides cross-table omni-directional interop between lakehouse table formats</li> + <li class="list-item why">Apache XTable™ is NOT a new or separate format, Apache XTable™ provides abstractions and tools for the translation of lakehouse table format metadata</li> + <li class="list-item why">Apache XTable™ is formerly known as OneTable</li> </ul> </div> <div id="w-node-b5fb012b-3ce1-44e6-09e8-015167aa018f-e51fabf1" class="w-layout-layout quick-stack wf-layout-layout"> @@ -105,7 +105,7 @@ <div class="what-text">Choose your <span class="text-span-3">destination</span> format(s)</div> </div> <div id="w-node-a5b4b7dd-251e-d9a7-855d-46427e19ad9b-e51fabf1" class="w-layout-cell what-cell"><img src="images/metadata-1.png" loading="lazy" alt="" class="image-4"> - <div class="what-text">XTable will translate the <span class="text-span-3">Metadata</span> layers</div> + <div class="what-text">Apache XTable™ will translate the <span class="text-span-3">Metadata</span> layers</div> </div> </div> </div> @@ -113,7 +113,7 @@ <section class="build-section"> <div class="w-layout-blockcontainer main-container w-container"> <div class="left-section"> - <h1 class="heading-section">Why <span class="text-span">build</span> XTable?</h1> + <h1 class="heading-section">Why <span class="text-span">build</span><br /> Apache XTable™?</h1> <ul role="list" class="list"> <li class="list-item">Choosing a table format is a costly evaluation</li> <li class="list-item">Each project has rich features that may fit different use-cases</li> @@ -130,7 +130,7 @@ <div class="div-block-24"> <div class="right-section-together"> <h1 class="heading-section-together">Let's <span class="text-span">build together</span></h1> - <div class="text-block">XTable is a community driven open source project. Come join us on Github and find easy ways to contribute.</div> + <div class="text-block">Apache XTable™ is a community driven open source project. Come join us on Github and find easy ways to contribute.</div> </div> <a href="https://github.com/apache/incubator-xtable"; class="primary-button w-button">Try it on GitHub</a> </div> @@ -149,34 +149,34 @@ <div class="icon-2 accordion-icon">keyboard_arrow_down</div> </div> <div class="accordion-item-content"> - <p class="faq-a">XTable reads the existing metadata of your table and writes out metadata for one or more other table formats by leveraging the existing APIs provided by each table format project. The metadata will be persisted under a directory in the base path of your table (_delta_log for Delta, metadata for Iceberg, and .hoodie for Hudi). This allows your existing data to be read as though it was written using Delta, Hudi, or Iceberg. For example, a Spark reader can use s [...] + <p class="faq-a">Apache XTable™ reads the existing metadata of your table and writes out metadata for one or more other table formats by leveraging the existing APIs provided by each table format project. The metadata will be persisted under a directory in the base path of your table (_delta_log for Delta, metadata for Iceberg, and .hoodie for Hudi). This allows your existing data to be read as though it was written using Delta, Hudi, or Iceberg. For example, a Spark reader c [...] </div> </div> <div class="accordion-item"> <div id="q1" class="accordion-item-trigger"> - <h4 class="accordion-heading"><span><strong class="faq-q">How is XTable different from Delta Lake Uniform?</strong></span></h4> + <h4 class="accordion-heading"><span><strong class="faq-q">How is Apache XTable™ different from Delta Lake Uniform?</strong></span></h4> <div class="icon-2 accordion-icon">keyboard_arrow_down</div> </div> <div class="accordion-item-content"> - <p class="faq-a">XTable provides abstraction interfaces that allow omni-directional interoperability across Delta, Hudi, Iceberg, and any other future lakehouse table formats such as Apache Paimon. XTable is a standalone github project that provides a neutral space for all the lakehouse table formats to constructively collaborate together.<br><br>Delta Lake Uniform is a one-directional conversion from Delta Lake to Apache Hudi or Apache Iceberg. Uniform is also governed insid [...] + <p class="faq-a">Apache XTable™ provides abstraction interfaces that allow omni-directional interoperability across Delta, Hudi, Iceberg, and any other future lakehouse table formats such as Apache Paimon. Apache XTable™ is a standalone github project that provides a neutral space for all the lakehouse table formats to constructively collaborate together.<br><br>Delta Lake Uniform is a one-directional conversion from Delta Lake to Apache Hudi or Apache Iceberg. Uniform is als [...] </div> </div> <div class="accordion-item"> <div id="q1" class="accordion-item-trigger"> - <h4 class="accordion-heading"><span><strong class="faq-q">When should I consider XTable?</strong></span></h4> + <h4 class="accordion-heading"><span><strong class="faq-q">When should I consider Apache XTable™?</strong></span></h4> <div class="icon-2 accordion-icon">keyboard_arrow_down</div> </div> <div class="accordion-item-content"> - <p class="faq-a">XTable can be used to easily switch between any of the table formats or even benefit from more than one simultaneously. Some organizations use XTable today because they have a diverse ecosystem of tools with polarized vendor support of table formats. Some users want lightning fast ingestion or indexing from Hudi and photon query accelerations of Delta Lake inside of Databricks. Some users want managed table services from Hudi, but also want write operations f [...] + <p class="faq-a">Apache XTable™ can be used to easily switch between any of the table formats or even benefit from more than one simultaneously. Some organizations use Apache XTable™ today because they have a diverse ecosystem of tools with polarized vendor support of table formats. Some users want lightning fast ingestion or indexing from Hudi and photon query accelerations of Delta Lake inside of Databricks. Some users want managed table services from Hudi, but also want wr [...] </div> </div> <div class="accordion-item"> <div id="q1" class="accordion-item-trigger"> - <h4 class="accordion-heading"><span><strong class="faq-q">Does XTable work in every cloud?</strong></span></h4> + <h4 class="accordion-heading"><span><strong class="faq-q">Does Apache XTable™ work in every cloud?</strong></span></h4> <div class="icon-2 accordion-icon">keyboard_arrow_down</div> </div> <div class="accordion-item-content"> - <p class="faq-a">Yes, anywhere that Delta, Iceberg, or Hudi work, XTable works.</p> + <p class="faq-a">Yes, anywhere that Delta, Iceberg, or Hudi work, Apache XTable™ works.</p> </div> </div> <div class="accordion-item"> @@ -185,7 +185,7 @@ <div class="icon-2 accordion-icon">keyboard_arrow_down</div> </div> <div class="accordion-item-content"> - <p class="faq-a">1. Hudi and Iceberg MoR tables not supported<br>2. Delta Delete Vectors are not supported<br>3. Synchronized transaction timestamps<br><br>With XTable you pick one primary format and one or more secondary formats. The write operations with the primary format work as normal. XTable than translates the metadata from the primary format to the secondaries. When committing the metadata of the secondary formats, the timestamp of the commit will not be the exact sam [...] + <p class="faq-a">1. Hudi and Iceberg MoR tables not supported<br>2. Delta Delete Vectors are not supported<br>3. Synchronized transaction timestamps<br><br>With Apache XTable™ you pick one primary format and one or more secondary formats. The write operations with the primary format work as normal. Apache XTable™ than translates the metadata from the primary format to the secondaries. When committing the metadata of the secondary formats, the timestamp of the commit will not [...] </div> </div> <div class="accordion-item"> @@ -203,7 +203,7 @@ <div class="icon-2 accordion-icon">keyboard_arrow_down</div> </div> <div class="accordion-item-content"> - <p class="faq-a">Follow XTable community channels on Linkedin and Twitter. Become a watcher on Github or reachout directly to any of the Github contributors to learn more.</p> + <p class="faq-a">Follow Apache XTable™ community channels on Linkedin and Twitter. Become a watcher on Github or reachout directly to any of the Github contributors to learn more.</p> </div> </div> <div class="accordion-item"> @@ -212,7 +212,7 @@ <div class="icon-2 accordion-icon">keyboard_arrow_down</div> </div> <div class="accordion-item-content"> - <p class="faq-a">Current contributors across Onehouse, Microsoft, Google, and others are planning to incubate XTable into the Apache Software Foundation. Stay tuned for more updates.</p> + <p class="faq-a">Current contributors across Onehouse, Microsoft, Google, and others are helping to incubate Apache XTable™ into the Apache Software Foundation. Stay tuned for more updates.</p> </div> </div> </div> @@ -231,8 +231,8 @@ <div class="footer__bottom text--center"><div class="footer__copyright" style="color: white;text-align: center;"> <div style="margin-top: 20px"> <a href="https://incubator.apache.org/"; target="_blank"><img style="height:40px; margin-bottom: 10px; margin-top: 10px" alt="Apache Software Foundation" src="/images/apache-incubator.svg"></a> - <p style="text-align:left; font-weight: 300; font-size: 0.8em;">Apache XTable is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the c [...] - <p style="text-align:left; font-weight: 300; font-size: 0.8em;">Copyright ©2024 Apache XTable, XTable, Apache, the Apache feather logo and the Apache XTable project logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</p> + <p style="text-align:left; font-weight: 300; font-size: 0.8em;">Apache XTable™ is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the [...] + <p style="text-align:left; font-weight: 300; font-size: 0.8em;">Copyright ©2024 Apache XTable™, XTable, Apache, the Apache feather logo and the Apache XTable™ project logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</p> </div> </div> </div>
