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Announcing BigQuery tables for Apache Iceberg | Google Cloud Blog

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class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-EehZO nRhiJb-fmcmS-oXtfBe"><h1 class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-ibL1re"><div class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-ibL1re"><span class="FewWi"></span>BigQuery tables for Apache Iceberg: optimized storage for the open lakehouse</div></h1></div></div><div class="nRhiJb-fmcmS-oXtfBe dEogG">October 12, 2024</div></div></section><div class="EKklye"><div class="nRhiJb-DARUcf ZWw7T"><div class="npzWPc"><div class="dzoHJ"><div class="nRhiJb-DX2B6 nRhiJb-DX2B6-OWXEXe-h30Snd"><div class="nRhiJb-j5y3u"><ul class="nRhiJb-Qijihe phRaUe" role="list"><li class="hpHPGf"><a class="nRhiJb-ARYxNe" href="https://x.com/intent/tweet?text=BigQuery%20tables%20for%20Apache%20Iceberg:%20optimized%20storage%20for%20the%20open%20lakehouse%20@googlecloud&amp;url=https://cloud.google.com/blog/products/data-analytics/announcing-bigquery-tables-for-apache-iceberg" track-name="x"track-type="social 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nRhiJb-LgbsSe-OWXEXe-CNusmb-o6Shpd " href="https://cloudonair.withgoogle.com/summit_series_24?utm_source=cgc-blog&amp;utm_medium=blog&amp;utm_campaign=summit-series-2024&amp;utm_content=summit-series-left-hand-rail-cta&amp;utm_term=-" track-metadata-module="left rail"track-metadata-module_headline="google cloud summit series" track-name="register"track-type="button"track-metadata-position="cta"track-metadata-eventdetail="cloudonair.withgoogle.com/summit_series_24?utm_source=cgc-blog&amp;utm_medium=blog&amp;utm_campaign=summit-series-2024&amp;utm_content=summit-series-left-hand-rail-cta&amp;utm_term=-">Register </a></div></div><div class="OYL9D nRhiJb-kR0ZEf-OWXEXe-GV1x9e-OiUrBf" jsname="tx2NYc"><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">For several years, BigQuery native tables have supported enterprise-level data management capabilities such as ACID transactions, streaming ingestion, and automatic storage optimizations. Many BigQuery customers store data in data lakes using open-source file formats such as Apache Parquet and table formats such as Apache Iceberg. In 2022, we launched </span><a href="https://cloud.google.com/bigquery/docs/biglake-intro"><span style="text-decoration:underline;vertical-align:baseline">BigLake tables</span></a><span style="vertical-align:baseline"> to allow customers to maintain a single copy of data and benefit from the security and performance offered by BigQuery. However, BigLake tables are currently read-only; BigQuery customers have to perform data mutations through external query engines and manually orchestrate data management. Another challenge is the </span><span style="font-style:italic;vertical-align:baseline">“small files problem”</span><span style="vertical-align:baseline"> during ingestion: because cloud object stores do not support appends, table writes need to be micro-batched, requiring trade-offs between performance and data consistency.</span></p> <p><span style="vertical-align:baseline">Today, we’re excited to announce the preview of BigQuery tables for Apache Iceberg, a fully managed, Apache Iceberg-compatible storage engine from BigQuery with features such as autonomous storage optimizations, clustering, and high-throughput streaming ingestion. BigQuery tables for Apache Iceberg use the Apache Iceberg format to store data in customer-owned cloud storage buckets while providing a similar customer experience and feature set as BigQuery native tables. Through BigQuery tables for Apache Iceberg, we are bringing a decade of BigQuery innovations to the lakehouse.</span></p></span></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_i96jpXM.max-2200x2200.jpg" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_i96jpXM.max-2200x2200.jpg" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_i96jpXM.max-2200x2200.jpg" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/1_i96jpXM.max-2200x2200.jpg" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">BigQuery tables for Apache Iceberg are writable from BigQuery through GoogleSQL data manipulation language (DML) and supports high-throughput streaming ingestion from open-source engines such as Apache Spark through BigQuery’s Write API. Here is an example to create a table with clustering:</span></p></span></section><section class="nRhiJb-DbgRPb-II5mzb-cGMI2b"><section><div class="kg99be" jscontroller="vkZm2d" jsaction="rcuQ6b:npT2md" data-text-to-copy="CREATE TABLE mydataset.taxi_trips CLUSTER BY vendor_id, pickup_datetime WITH CONNECTION `us.myconnection` OPTIONS ( storage_uri=&#39;gs://mybucket/taxi_trips&#39;, table_format=&#39;ICEBERG&#39;, file_format=&#39;PARQUET&#39; ) AS SELECT * FROM `bigquery-public-data.new_york_taxi_trips.tlc_green_trips_2020`;"><div jsaction="click:VTVhdd" class="GaRnv"><svg class="" viewBox="0 0 20 20" role="presentation" aria-hidden="true" height=20 widdht=20><path d="M7.979 14.667c-.458 0-.84-.153-1.146-.459a1.53 1.53 0 0 1-.458-1.125V3.938c0-.459.153-.84.458-1.146.306-.306.688-.459 1.146-.459h6.625c.445 0 .823.156 1.136.469.312.313.468.691.468 1.136v9.145c0 .445-.156.82-.468 1.125a1.564 1.564 0 0 1-1.136.459H7.979Zm0-1.334h6.625c.083 0 .15-.024.198-.073a.239.239 0 0 0 .073-.177V3.938a.265.265 0 0 0-.073-.198.268.268 0 0 0-.198-.073H7.979a.268.268 0 0 0-.198.073.266.266 0 0 0-.073.198v9.145c0 .07.024.129.073.177a.267.267 0 0 0 .198.073ZM4.875 17.75a1.53 1.53 0 0 1-1.125-.458 1.532 1.532 0 0 1-.458-1.125V5.729h1.333v10.438c0 .07.024.128.073.177a.24.24 0 0 0 .177.073h7.937v1.333H4.875ZM7.708 3.667v9.666-9.666Z" fill="#1a73e8"/></svg></div><div jscontroller="M2lJBf" jsaction="rcuQ6b:npT2md" data-enable-content-editable-input-style="false" data-enable-line-wrapping data-read-only><div class="hg3Lgc qs41qe BEXrzd" data-loadingmessage="Loading..." jscontroller="GFartf" jsaction="animationend:kWijWc;dyRcpb:dyRcpb" data-active="true" jsname="aZ2wEe"><div class="eBrXtc" aria-live="polite" jsname="vyyg5">Loading...</div><div jsname="Hxlbvc" class="JdM54e"><div class="aopPX ZqnFk co39ub"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fxjES Cn087"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX ZHXbZe hfsr6b"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div><div class="aopPX fDBOYb EjXFBf"><div class="e2XBBf uEtL3"><div class="kPEoYc GOJTSe"></div></div><div class="LqC3Y"><div class="kPEoYc GOJTSe"></div></div><div class="e2XBBf QR7YS"><div class="kPEoYc GOJTSe"></div></div></div></div></div><div dir="ltr"><textarea jsname="bqeLof" class="Fdco1c">CREATE TABLE mydataset.taxi_trips CLUSTER BY vendor_id, pickup_datetime WITH CONNECTION `us.myconnection` OPTIONS ( storage_uri=&#39;gs://mybucket/taxi_trips&#39;, table_format=&#39;ICEBERG&#39;, file_format=&#39;PARQUET&#39; ) AS SELECT * FROM `bigquery-public-data.new_york_taxi_trips.tlc_green_trips_2020`;</textarea></div></div></div><p class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb TxA6Xc"></p></section></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><strong style="vertical-align:baseline">Fully managed enterprise storage for the lakehouse</strong></p> <p><span style="vertical-align:baseline">BigQuery tables for Apache Iceberg address the limitations of open-source table formats. With BigQuery tables for Apache Iceberg, BigQuery takes care of table-maintenance tasks autonomously without customer toil. BigQuery keeps the table optimized by combining smaller files into optimal file sizes, providing automatic re-clustering of data and garbage collection of files. For instance, optimal file sizes are adaptively determined based on the size of the table. BigQuery tables for Apache Iceberg benefit from over a decade of expertise running automated storage optimization for BigQuery native tables efficiently and cost-effectively. There is no need to run </span><span style="font-style:italic;vertical-align:baseline">OPTIMIZE</span><span style="vertical-align:baseline"> or </span><span style="font-style:italic;vertical-align:baseline">VACUUM</span><span style="vertical-align:baseline"> manually.</span></p> <p><span style="vertical-align:baseline">For high-throughput streaming ingestion, BigQuery tables for Apache Iceberg leverage </span><a href="https://research.google/pubs/vortex-a-stream-oriented-storage-engine-for-big-data-analytics/" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">Vortex</span></a><span style="vertical-align:baseline">, an exabyte-scale structured storage system that powers the BigQuery storage write API. BigQuery tables for Apache Iceberg durably store recently ingested tuples in a row-oriented format and periodically convert them to Parquet. The high-throughput ingestion and parallel reads are supported through the open-source Spark and Flink BigQuery connectors. </span><a href="https://cloud.google.com/pubsub/docs/bigquery"><span style="text-decoration:underline;vertical-align:baseline">Pub/Sub</span></a><span style="vertical-align:baseline"> and Datastream can ingest data into BigQuery tables for Apache Iceberg, so you don’t need to maintain bespoke infrastructure.</span></p> <p><span style="vertical-align:baseline">BigQuery tables for Apache Iceberg store table metadata in BigQuery’s </span><a href="https://vldb.org/pvldb/vol14/p3083-edara.pdf" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">scalable metadata management system</span></a><span style="vertical-align:baseline">. BigQuery stores fine-grained metadata and uses distributed query processing and data management techniques to handle metadata. As a result, BigQuery tables for Apache Iceberg aren’t constrained by needing to commit the metadata to object stores, allowing a higher rate of mutations than what is possible with table formats. Since writers cannot directly mutate the transaction log, the table metadata is tamper-proof, and has a reliable audit history.</span></p> <p><span style="vertical-align:baseline">BigQuery tables for Apache Iceberg continue to support fine-grained security policies enforced by the storage APIs while extending support for governance policy management, data quality and end-to-end lineage through Dataplex.</span></p></span></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/original_images/image2_IOjgjdq.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/original_images/image2_IOjgjdq.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/original_images/image2_IOjgjdq.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/original_images/image2_IOjgjdq.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure></section></div></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">BigQuery tables for Apache Iceberg export metadata into Iceberg snapshots in cloud storage. The pointer to the latest exported metadata will soon be registered in BigQuery metastore, a serverless runtime metadata service </span><a href="https://cloud.google.com/blog/products/data-analytics/bigquery-is-a-unified-ai-ready-data-analytics-platform"><span style="text-decoration:underline;vertical-align:baseline">announced</span></a><span style="vertical-align:baseline"> earlier this year. Iceberg metadata exports allows any engine capable of understanding Iceberg to query the data directly from Cloud Storage. </span></p> <h3><span style="vertical-align:baseline">Learn more</span></h3> <p><span style="vertical-align:baseline">Customers like HCA Healthcare, one of the largest health care providers in the world, see value in leveraging BigQuery tables for Apache Iceberg as their Apache Iceberg-compatible storage layer from BigQuery, making new lakehouse use-cases possible. The preview of BigQuery tables for Apache Iceberg is available in all Google Cloud regions today. You can get started today by following the </span><a href="https://cloud.google.com/bigquery/docs/iceberg-tables"><span style="text-decoration:underline;vertical-align:baseline">documentation</span></a><span style="vertical-align:baseline">.</span></p></span></section><section class="kcBhad"><section class="Fabbec"><span class="WrMNjb">Posted in</span><ul class="FzXI4e"><li class="I4B51b"><a href="https://cloud.google.com/blog/products/data-analytics" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/products/data-analytics"track-metadata-module="tag list"track-metadata-module_headline="posted in">Data Analytics</a></li><li class="I4B51b"><a href="https://cloud.google.com/blog/products/streaming" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/products/streaming"track-metadata-module="tag list"track-metadata-module_headline="posted in">Streaming</a></li></ul></section></section></div></div></div></div></div><section 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class=" QaGyvd nRhiJb-kR0ZEf-OWXEXe-GV1x9e-c5RTEf nRhiJb-kR0ZEf-OWXEXe-GV1x9e-qWD73c-V2iZpe"><div class="mA0uBe"><a href="https://cloud.google.com/blog/products/data-analytics/inside-deutsche-banks-pbdp-retail-data-platform" class="lD2oe" track-name="how deutsche bank built a new retail data platform on google cloud"track-type="card"track-metadata-eventdetail="cloud.google.com/blog/products/data-analytics/inside-deutsche-banks-pbdp-retail-data-platform"><div class="AhkbS "><div class="hqnDEf"><section class="PBkdHd "><img class=" D5RK8d" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg" loading="lazy"/></section></div><div class="JUOx5b"><div class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c nRhiJb-DbgRPb-c5RTEf-ma6Yeb nRhiJb-BFbNVe-r8s4j-bMElCd FI6Gl nRhiJb-fmcmS-oXtfBe" track-name="data 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,[[97517170,48830069,97863170,93874002,1706538,97684533,97535270,97442197,93778619,97656897,97785986,48554501,48887080,97863042,1714249,97716265,48489833,97517154,93873986,97684517,97442181,97656881,97785970,48887064],null,null,null,null,true],]; window.IJ_valuesCb && window.IJ_valuesCb();</script><script nonce="BduWwXdRJ2eyjytPfLtaEw">(function(){'use strict';var c=window,d=[];c.aft_counter=d;var e=[],f=0;function _recordIsAboveFold(a){if(!c._isLazyImage(a)&&!a.hasAttribute("data-noaft")&&a.src){var b=(c._isVisible||function(){})(c.document,a);a.setAttribute("data-atf",b);b&&(e.indexOf(a)!==-1||d.indexOf(a)!==-1||a.complete||d.push(a),a.hasAttribute("data-iml")&&(a=Number(a.getAttribute("data-iml")),a>f&&(f=a)))}} c.initAft=function(){f=0;e=Array.prototype.slice.call(document.getElementsByTagName("img")).filter(function(a){return!!a.getAttribute("data-iml")});[].forEach.call(document.getElementsByTagName("img"),function(a){try{_recordIsAboveFold(a)}catch(b){throw b.message=a.hasAttribute("data-iid")?b.message+"\nrecordIsAboveFold error for defer inlined image":b.message+("\nrecordIsAboveFold error for img element with <src: "+a.src+">"),b;}});if(d.length===0)c.onaft(f)};}).call(this); initAft()</script><script class="ds:0" nonce="BduWwXdRJ2eyjytPfLtaEw">AF_initDataCallback({key: 'ds:0', hash: '1', data:[["Announcing BigQuery tables for Apache Iceberg","The BigQuery tables for Apache Iceberg storage engine offers autonomous storage optimizations, clustering, and high-throughput streaming ingestion.",[1728694800],"https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-2600x2600.jpg","https://cloud.google.com/blog/products/data-analytics/announcing-bigquery-tables-for-apache-iceberg",[1728687609,206224000]],[["Anoop Johnson","Principal Engineer"],["Dylan Myers","Group Product Manager"]],[null,"\u003cscript type\u003d\"application/ld+json\"\u003e{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"@id\":\"https://cloud.google.com/blog/products/data-analytics/announcing-bigquery-tables-for-apache-iceberg\",\"headline\":\"Announcing BigQuery tables for Apache Iceberg\",\"description\":\"The BigQuery tables for Apache Iceberg storage engine offers autonomous storage optimizations, clustering, and high-throughput streaming ingestion.\",\"image\":\"https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-2600x2600.jpg\",\"author\":[{\"@type\":\"Person\",\"name\":\"Anoop Johnson\",\"url\":\"\"},{\"@type\":\"Person\",\"name\":\"Dylan Myers\",\"url\":\"\"}],\"datePublished\":\"2024-10-12\",\"publisher\":{\"@type\":\"Organization\",\"name\":\"Google Cloud\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https://www.gstatic.com/devrel-devsite/prod/v8bb8fa0afe9a8c3a776ebeb25d421bb443344d789b3607754dfabea418b8c4be/cloud/images/cloud-logo.svg\"}},\"url\":\"https://cloud.google.com/blog/products/data-analytics/announcing-bigquery-tables-for-apache-iceberg\",\"keywords\":[\"Data Analytics\",\"Streaming\"],\"timeRequired\":\"PT3M\"}\u003c/script\u003e"],["BigQuery tables for Apache Iceberg: optimized storage for the open lakehouse"],null,null,[[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eFor several years, BigQuery native tables have supported enterprise-level data management capabilities such as ACID transactions, streaming ingestion, and automatic storage optimizations. Many BigQuery customers store data in data lakes using open-source file formats such as Apache Parquet and table formats such as Apache Iceberg. In 2022, we launched \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/bigquery/docs/biglake-intro\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eBigLake tables\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e to allow customers to maintain a single copy of data and benefit from the security and performance offered by BigQuery. However, BigLake tables are currently read-only; BigQuery customers have to perform data mutations through external query engines and manually orchestrate data management. Another challenge is the \u003c/span\u003e\u003cspan style\u003d\"font-style:italic;vertical-align:baseline\"\u003e\u201csmall files problem\u201d\u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e during ingestion: because cloud object stores do not support appends, table writes need to be micro-batched, requiring trade-offs between performance and data consistency.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eToday, we\u2019re excited to announce the preview of BigQuery tables for Apache Iceberg, a fully managed, Apache Iceberg-compatible storage engine from BigQuery with features such as autonomous storage optimizations, clustering, and high-throughput streaming ingestion. BigQuery tables for Apache Iceberg use the Apache Iceberg format to store data in customer-owned cloud storage buckets while providing a similar customer experience and feature set as BigQuery native tables. Through BigQuery tables for Apache Iceberg, we are bringing a decade of BigQuery innovations to the lakehouse.\u003c/span\u003e\u003c/p\u003e"]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/images/1_i96jpXM.max-2200x2200.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/1_i96jpXM.max-1100x1100.jpg 1060w, https://storage.googleapis.com/gweb-cloudblog-publish/images/1_i96jpXM.max-2200x2200.jpg 2120w"," 1060px, 2120px","https://storage.googleapis.com/gweb-cloudblog-publish/images/1_i96jpXM.max-2200x2200.jpg"],null,3]]],[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eBigQuery tables for Apache Iceberg are writable from BigQuery through GoogleSQL data manipulation language (DML) and supports high-throughput streaming ingestion from open-source engines such as Apache Spark through BigQuery\u2019s Write API. Here is an example to create a table with clustering:\u003c/span\u003e\u003c/p\u003e"]]],[["CREATE TABLE mydataset.taxi_trips\r\nCLUSTER BY vendor_id, pickup_datetime\r\nWITH CONNECTION `us.myconnection`\r\nOPTIONS (\r\n storage_uri\u003d'gs://mybucket/taxi_trips',\r\n table_format\u003d'ICEBERG',\r\n file_format\u003d'PARQUET'\r\n)\r\nAS SELECT * FROM `bigquery-public-data.new_york_taxi_trips.tlc_green_trips_2020`;",null,[null,""]]],[null,null,[null,[null,"\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eFully managed enterprise storage for the lakehouse\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eBigQuery tables for Apache Iceberg address the limitations of open-source table formats. With BigQuery tables for Apache Iceberg, BigQuery takes care of table-maintenance tasks autonomously without customer toil. BigQuery keeps the table optimized by combining smaller files into optimal file sizes, providing automatic re-clustering of data and garbage collection of files. For instance, optimal file sizes are adaptively determined based on the size of the table. BigQuery tables for Apache Iceberg benefit from over a decade of expertise running automated storage optimization for BigQuery native tables efficiently and cost-effectively. There is no need to run \u003c/span\u003e\u003cspan style\u003d\"font-style:italic;vertical-align:baseline\"\u003eOPTIMIZE\u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e or \u003c/span\u003e\u003cspan style\u003d\"font-style:italic;vertical-align:baseline\"\u003eVACUUM\u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e manually.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eFor high-throughput streaming ingestion, BigQuery tables for Apache Iceberg leverage \u003c/span\u003e\u003ca href\u003d\"https://research.google/pubs/vortex-a-stream-oriented-storage-engine-for-big-data-analytics/\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eVortex\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e, an exabyte-scale structured storage system that powers the BigQuery storage write API. BigQuery tables for Apache Iceberg durably store recently ingested tuples in a row-oriented format and periodically convert them to Parquet. The high-throughput ingestion and parallel reads are supported through the open-source Spark and Flink BigQuery connectors. \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/pubsub/docs/bigquery\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003ePub/Sub\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e and Datastream can ingest data into BigQuery tables for Apache Iceberg, so you don\u2019t need to maintain bespoke infrastructure.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eBigQuery tables for Apache Iceberg store table metadata in BigQuery\u2019s \u003c/span\u003e\u003ca href\u003d\"https://vldb.org/pvldb/vol14/p3083-edara.pdf\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003escalable metadata management system\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e. BigQuery stores fine-grained metadata and uses distributed query processing and data management techniques to handle metadata. As a result, BigQuery tables for Apache Iceberg aren\u2019t constrained by needing to commit the metadata to object stores, allowing a higher rate of mutations than what is possible with table formats. Since writers cannot directly mutate the transaction log, the table metadata is tamper-proof, and has a reliable audit history.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eBigQuery tables for Apache Iceberg continue to support fine-grained security policies enforced by the storage APIs while extending support for governance policy management, data quality and end-to-end lineage through Dataplex.\u003c/span\u003e\u003c/p\u003e"]]],[null,null,null,null,null,null,null,null,[[[null,""],["https://storage.googleapis.com/gweb-cloudblog-publish/original_images/image2_IOjgjdq.png","https://storage.googleapis.com/gweb-cloudblog-publish/original_images/image2_IOjgjdq.png 1060w, https://storage.googleapis.com/gweb-cloudblog-publish/original_images/image2_IOjgjdq.png 1999w"," 1060px, 1999px","https://storage.googleapis.com/gweb-cloudblog-publish/original_images/image2_IOjgjdq.png"],null,3]]],[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eBigQuery tables for Apache Iceberg export metadata into Iceberg snapshots in cloud storage. The pointer to the latest exported metadata will soon be registered in BigQuery metastore, a serverless runtime metadata service \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/blog/products/data-analytics/bigquery-is-a-unified-ai-ready-data-analytics-platform\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eannounced\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e earlier this year. Iceberg metadata exports allows any engine capable of understanding Iceberg to query the data directly from Cloud Storage.\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eLearn more\u003c/span\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eCustomers like HCA Healthcare, one of the largest health care providers in the world, see value in leveraging BigQuery tables for Apache Iceberg as their Apache Iceberg-compatible storage layer from BigQuery, making new lakehouse use-cases possible. The preview of BigQuery tables for Apache Iceberg is available in all Google Cloud regions today. You can get started today by following the \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/bigquery/docs/iceberg-tables\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003edocumentation\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e.\u003c/span\u003e\u003c/p\u003e"]]]],[["Data Analytics","Dataproc Serverless: Now faster, easier and smarter","Running Dataproc Serverless now has Native query execution, integrated monitoring, and Gemini-powered proactive autotuning and troubleshooting.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg"],null,7,null,"https://cloud.google.com/blog/products/data-analytics/dataproc-serverless-performance-and-usability-updates",null,1,[["Pardha Saradhi Uppala","Product Manager"],["Virat Bansal","Software Engineer"]],null,"55584"],["Data Analytics","Secure your data ecosystem: a multi-layered approach with Google Cloud","A new ebook, Building a Secure Data Platform with Google Cloud, details the tools available to protect your data as you use it to grow your business.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg"],null,3,null,"https://cloud.google.com/blog/products/data-analytics/learn-how-to-build-a-secure-data-platform-with-google-cloud-ebook",null,1,[["Nic Smith","Head of Product Marketing, Data \u0026 Analytics"],["Robert Sadowski","Director, Product Marketing, Security \u0026 Trust"]],null,"55573"],["Data Analytics","How Deutsche Bank built a new retail data platform on Google Cloud","A closer look at Deutsche Bank\u2019s Private Bank data platform \u2014 its requirements, architecture, services, and management and operations.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/09_-_Data_Analytics_tFH57V6.max-700x700.jpg"],null,7,null,"https://cloud.google.com/blog/products/data-analytics/inside-deutsche-banks-pbdp-retail-data-platform",null,1,[["Vladimir Elvov","Lead Customer Engineer, Data \u0026 Analytics"],["Lars Fockele","Lead Data Engineer, Deutsche Bank AG"]],null,"55554"],["Data Analytics","How PUMA leverages built-in intelligence with BigQuery for greater customer engagement","To improve its customer engagement and prepare for a cookieless future via advanced audience segmentation, PUMA partnered with Google Cloud.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/puma-website-store-suede-xl_1.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/puma-website-store-suede-xl_1.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/puma-website-store-suede-xl_1.max-700x700.jpg 648w"," 324px, 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