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Materialized views support several different use cases, including performance. In this blog post, I’ll focus on using materialized views to solve a specific performance problem that arises when a large data set has multiple access paths. The example presented in this post shows a 10 billion row table and two different ways to query the data.\u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_1":{"id":"blog-title-c37c9bfe86","propertiesId":"the-setup","type":"heading2","lines":["The Setup"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_2":{"id":"blog-text-e01c044c41","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EImagine having a weblog that keeps track of certain metrics that are accessible by different attributes (\u003Cstrong\u003EIDs\u003C/strong\u003E) for different use cases such as by timestamp and \u003Cstrong\u003EPAGE_ID\u003C/strong\u003E. For this example, let’s assume that one of the metrics tracked in the weblog table is the time it takes to load a page in milliseconds (\u003Cstrong\u003ETIME_TO_LOAD_MS\u003C/strong\u003E). \u003C/span\u003E\u003C/p\u003E\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003ETo simplify the setup, the table below illustrates using bigint as the data type for the \u003Cstrong\u003EIDs\u003C/strong\u003E. In a real-world use case, data types can be anything supported by Snowflake; however, to increase the size of the data set and make the use case more realistic, I’m adding attributes (\u003Cstrong\u003Emetric2 through metric9\u003C/strong\u003E).\u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1":{"id":"code-snippet-ad63ecb9dc","fileName":"","language":"","codeSnippet":"CREATE OR REPLACE TABLE WEBLOG (\n  CREATE_MS BIGINT,\n  PAGE_ID BIGINT,\n  TIME_TO_LOAD_MS INTEGER,\n  METRIC2 INTEGER,\n  METRIC3 INTEGER,\n  METRIC4 INTEGER,\n  METRIC5 INTEGER,\n  METRIC6 INTEGER,\n  METRIC7 INTEGER,\n  METRIC8 INTEGER,\n  METRIC9 INTEGER\n);","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_3":{"id":"blog-text-00f9a5755f","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EI want to compute the average load time for two different use cases. In use case \u003C/span\u003E\u003Cb\u003EA), \u003C/b\u003E\u003Cspan style=\"font-weight: 400;\"\u003EI'll focus on the average load for a particular timeframe across all pages. For use case \u003C/span\u003E\u003Cb\u003EB),\u003C/b\u003E\u003Cspan style=\"font-weight: 400;\"\u003E I'll focus on the average load time for a particular \u003Cstrong\u003EPage_ID\u003C/strong\u003E. Use-case \u003Cstrong\u003EA)\u003C/strong\u003E is satisfied by a query directly against the table marked \u003Cstrong\u003EWEBLOG. \u003C/strong\u003EIn this instance the query returns quickly because the data is clustered naturally by the creation date. \u003C/span\u003E\u003Cspan style=\"font-weight: 400;\"\u003EUse-case \u003C/span\u003E\u003Cb\u003EB)\u003C/b\u003E\u003Cspan style=\"font-weight: 400;\"\u003E requires more time, however, because the data is filtered by \u003Cstrong\u003EPage_ID\u003C/strong\u003E, yet it's clustered by the creation date, \u003Cstrong\u003ECREATE_MS\u003C/strong\u003E.\u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_2":{"id":"blog-title-9fccdf16ad","propertiesId":"the-problem-multiple-access-paths","type":"heading2","lines":["The Problem: Multiple Access Paths"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_4":{"id":"blog-text-8e9fca28d8","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003ETo illustrate the problem, I’ll generate a big data set (10 billion rows). The data flows into the \u003Cstrong\u003EWEBLOG\u003C/strong\u003E table sequentially when assuming that \u003Cstrong\u003ECREATE_MS\u003C/strong\u003E is measured in milliseconds and the data for the \u003Cstrong\u003EPage_ID\u003C/strong\u003E is random. \u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_2":{"id":"code-snippet-63f4013c9b","fileName":"","language":"","codeSnippet":"INSERT INTO WEBLOG\nSELECT\n   (SEQ8())::BIGINT AS CREATE_MS\n   ,UNIFORM(1,9999999,RANDOM(10002))::BIGINT PAGE_ID\n   ,UNIFORM(1,9999999,RANDOM(10003))::INTEGER TIME_ON_LOAD_MS\n   ,UNIFORM(1,9999999,RANDOM(10005))::INTEGER METRIC2\n   ,UNIFORM(1,9999999,RANDOM(10006))::INTEGER METRIC3\n   ,UNIFORM(1,9999999,RANDOM(10007))::INTEGER METRIC4\n   ,UNIFORM(1,9999999,RANDOM(10008))::INTEGER METRIC5\n   ,UNIFORM(1,9999999,RANDOM(10009))::INTEGER METRIC6\n   ,UNIFORM(1,9999999,RANDOM(10010))::INTEGER METRIC7\n   ,UNIFORM(1,9999999,RANDOM(10011))::INTEGER METRIC8\n   ,UNIFORM(1,9999999,RANDOM(10012))::INTEGER METRIC9\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 10000000000))\nORDER BY CREATE_ms;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_5":{"id":"blog-text-ac5ac10756","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EA key indicator for estimating performance in Snowflake is the clustering depth for the filter attribute. To retrieve clustering depth for an attribute on a table, first specify the clustering attribute with an \u003Cstrong\u003EALTER TABLE\u003C/strong\u003E statement.\u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_3":{"id":"code-snippet-ef203d5e7e","fileName":"","language":"","codeSnippet":"ALTER TABLE WEBLOG CLUSTER BY (CREATE_MS);\n\nSELECT SYSTEM$CLUSTERING_INFORMATION( 'WEBLOG' , '(CREATE_MS)' );\nSELECT SYSTEM$CLUSTERING_INFORMATION( 'WEBLOG' , '(PAGE_ID)' );","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_6":{"id":"blog-text-7b0d1e7206","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EClustering depth for attribute \u003Cstrong\u003ECREATE_MS\u003C/strong\u003E is a good option because it offers a small value. In this case it’s \u003Cstrong\u003E~1\u003C/strong\u003E, but that is suboptimal (a large value) for \u003Cstrong\u003EPAGE_ID\u003C/strong\u003E. However, because I created the data sorted by \u003Cstrong\u003ECREATE_MS\u003C/strong\u003E, this is an expected outcome. On the other hand, \u003Cstrong\u003EPAGE_ID\u003C/strong\u003E is randomly distributed.\u003C/span\u003E\u003C/p\u003E\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003ERunning a query against \u003Cstrong\u003EWEBLOG\u003C/strong\u003E confirms the problem. Making the problem easily visible requires running a relatively small warehouse for a 10 billion row data set. The numbers below are based on a \u003Cstrong\u003EMEDIUM\u003C/strong\u003E cluster. \u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_4":{"id":"code-snippet-a348f08f8e","fileName":"","language":"","codeSnippet":"SELECT COUNT(*) CNT, AVG(TIME_TO_LOAD_MS) AVG_TIME_TO_LOAD\nFROM WEBLOG\nWHERE CREATE_MS BETWEEN 1000000000 AND 1000001000;\nSELECT COUNT(*) CNT, AVG(TIME_TO_LOAD_MS)  AVG_TIME_TO_LOAD\nFROM WEBLOG\nWHERE PAGE_ID=100000;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_7":{"id":"blog-text-ef89704ae4","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EBoth queries summarize approximately 1000 rows. The first query runs in about 0.1 secs and the second requires roughly 100 secs (assuming a cold data cache). The substantial difference occurs because the first query examines only one micro partition, while the second query scans approximately 21,000. \u003C/span\u003E\u003C/p\u003E\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003ERunning both queries with equal performance requires using a second copy of the data that’s organized differently, hence optimizing access for both query patterns. \u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_3":{"id":"blog-title-9395dd73ef","propertiesId":"the-solution-clustered-materialized-view","type":"heading2","lines":["The Solution: Clustered Materialized View"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_8":{"id":"blog-text-deffd9ed41","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EThe solution to the problem lies with two new features in Snowflake: materialized views and auto-clustering.\u003C/span\u003E\u003C/p\u003E\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003ECreating the materialized view with Snowflake allows you to specify the new clustering key, which enables Snowflake to reorganize the data during the initial creation of the materialized view.\u003C/span\u003E\u003C/p\u003E\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EFor a source table of about 10 billion rows, a \u003Cstrong\u003EMEDIUM-\u003C/strong\u003Esized warehouse takes about 20 minutes to create the materialized view. To increase performance and reduce the time to create the test date, I used another feature in Snowflake, which is the ability to elastically resize the warehouse. \u003C/span\u003E\u003Cspan style=\"font-weight: 400;\"\u003EWhen running the code below, you need to change the placeholder \u003Cstrong\u003E<name>\u003C/strong\u003E with the name of your own warehouse.\u003C/span\u003E\u003C/p\u003E\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EUsing the example below decreases the initial build time for the materialized view, making it 16X faster than running the same statement on a \u003Cstrong\u003EMEDIUM-\u003C/strong\u003Esized cluster. When running the code shown below, change the placeholder <name> with the name of your warehouse.\u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_5":{"id":"code-snippet-99af988e2e","fileName":"","language":"","codeSnippet":"ALTER WAREHOUSE \u003CNAME\u003E SET WAREHOUSE_SIZE=XXXLARGE;\n\nCREATE OR REPLACE MATERIALIZED VIEW MV_TIME_TO_LOAD\n     (CREATE_MS, PAGE_ID, TIME_TO_LOAD_MS) CLUSTER BY (PAGE_ID)\n  AS\n     SELECT CREATE_MS, PAGE_ID, TIME_TO_LOAD_MS\n     FROM WEBLOG;\n\nALTER WAREHOUSE \u003CNAME\u003E SET WAREHOUSE_SIZE=MEDIUM;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_9":{"id":"blog-text-81f9f2392e","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EAfter completing the materialized view build, validate optimal data distribution by retrieving the clustering information. \u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_6":{"id":"code-snippet-a09dfe7a74","fileName":"","language":"","codeSnippet":"SELECT SYSTEM$CLUSTERING_INFORMATION\n  ( 'MV_TIME_TO_LOAD' , '(PAGE_ID)' );","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_10":{"id":"blog-text-7321619eb1","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EThe clustering depth, which should be \u003Cstrong\u003E~2\u003C/strong\u003E, provides an indication that a query against the materialized view by \u003Cstrong\u003EPAGE_ID\u003C/strong\u003E should be much faster. How much faster? \u003C/span\u003E\u003C/p\u003E\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003ETo get a baseline, I first ran the query against the base table. Achieving more reproducible results requires limiting the impact of query results caching. Query results caching is another great feature in Snowflake; however, when comparing performance, you want to compare apples to apples. For that reason, I disabled the cached results feature. \u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_7":{"id":"code-snippet-934179f20b","fileName":"","language":"","codeSnippet":"ALTER SESSION SET USE_CACHED_RESULT=FALSE;\n\nSELECT COUNT(*),AVG(TIME_TO_LOAD_MS)\nFROM WEBLOG\nWHERE PAGE_ID=100000;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_11":{"id":"blog-text-b84dbed04a","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EThe second query doesn’t take the roughly 100 seconds that it did when running the query for the first time. This time it takes closer to 20 secs. Why? Because now you’re seeing the impact of Snowflake providing both result caching and micro partition caching on the execution cluster. If this was the first time running this query, and the cluster cache was cold, it would still require about 100 seconds.\u003C/span\u003E\u003C/p\u003E\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003ENow, let’s check the runtime for the same query against the materialized view. Testing shows the results taking about 0.1 seconds, which is similar to the lookup by \u003Cstrong\u003ECREATE_MS\u003C/strong\u003E against the base table. \u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_8":{"id":"code-snippet-a562fa5d4f","fileName":"","language":"","codeSnippet":"ALTER SESSION SET USE_CACHED_RESULT=FALSE;\n\nSELECT COUNT(*),AVG(TIME_TO_LOAD_MS)  AVG_TIME_TO_LOAD\nFROM MV_TIME_TO_LOAD\nWHERE PAGE_ID=100000;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_12":{"id":"blog-text-f76e2dcc99","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EThis is a substantial improvement. However, the scenario above, by itself, isn’t that impressive because creating a new, materialized version of the base table through a Create Table AS (CTAS) statement accomplishes the same thing. So, what happens when the data in the base table changes? The data in the CTAS version immediately becomes stale.\u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_4":{"id":"blog-title-ac63b34291","propertiesId":"putting-it-all-together","type":"heading2","lines":["Putting It All Together"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_13":{"id":"blog-text-cc197b9b23","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EThe real power of materialized views stems from the fact that all \u003Cstrong\u003EDML\u003C/strong\u003E changes (\u003Cstrong\u003EINSERT, UPDATE, DELETE\u003C/strong\u003E) to the base table (WEBLOG) are automatically applied to the materialized view. Not only are the changes be applied, they’re applied in a transactionally consistent way. Now, at any point in time, the data in the materialized view will match the data for the view query when applying it to the source table. Lastly, having a materialized view on top of a source table doesn’t impact the \u003Cstrong\u003EDML\u003C/strong\u003E performance against the source table.\u003C/span\u003E\u003C/p\u003E\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003ETo simulate activity against the source table, I wrote a small Python program that inserts batches of rows with a configurable number of seconds in between batches. \u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_9":{"id":"code-snippet-6379d823d3","fileName":"","language":"","codeSnippet":"import snowflake.connector\nimport time\nimport sys\n\ndef getSnowFlakeCntxt():\n   # Set SnowFlake context.\n   SnowFlakectx = snowflake.connector.connect(\n       user=\u003Cuser\u003E,\n       password=\u003Cpassword\u003E,\n       account=\u003Caccount\u003E\n       warehouse=\u003Cwarehouse\u003E,\n       database=\u003Cdatabase\u003E,\n       schema=\u003Cschema\u003E\n)\nreturn SnowFlakectx\n\ndef executeSnowflakeCmd(iteration,batch,seed):\n   conn = getSnowFlakeCntxt()\n   cs = conn.cursor()\n   try:\n       cs.execute(\"alter session set QUERY_TAG = %s\", (\"MV_INSERT_TEST\"))\n       insSQL = \"\"\"\n          insert into WEBLOG\n          select\n             ((seq8())+\"\"\" + str(iteration*batch+seed) +\"\"\")::bigint as create_ms\n             ,uniform(1,9999999,random(10002))::bigint page_id\n             ,uniform(1,9999999,random(10003))::integer time_to_load_ms\n             ,uniform(1,9999999,random(10005))::integer metric2\n             ,uniform(1,9999999,random(10006))::integer metric3\n             ,uniform(1,9999999,random(10007))::integer metric4\n             ,uniform(1,9999999,random(10008))::integer metric5\n             ,uniform(1,9999999,random(10009))::integer metric6\n             ,uniform(1,9999999,random(10010))::integer metric7\n             ,uniform(1,9999999,random(10011))::integer metric8\n             ,uniform(1,9999999,random(10012))::integer metric9\n          from table(generator(rowcount =\u003E \"\"\" +str(batch) + \"\"\"))\n          order by create_ms\n       \"\"\"\n       cs.execute(insSQL)\n   finally:\n       cs.close()\n\n\nif __name__ == \"__main__\":\n   iterations=int(sys.argv[1])\n   batch_size=int(sys.argv[2])\n   sleep_time=int(sys.argv[3])\n   seed=int(sys.argv[4])\n   for i in range(0,iterations):\n       executeSnowflakeCmd(i,batch_size,seed)\n       time.sleep(sleep_time)\n   print(\"***** COMPLETED ******\")","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_14":{"id":"blog-text-2d3a07b5c8","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EThe statement below invokes the script with 20 iterations and each iteration inserts 600k rows, with 60 seconds between batches. The last parameter for the script is a seed value to create new rows with an \u003Cstrong\u003EID\u003C/strong\u003E greater than the existing rows.\u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_10":{"id":"code-snippet-2cb6d00562","fileName":"","language":"","codeSnippet":"python TableInsert.py 20 600000 60 100000000","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_15":{"id":"blog-text-efb32e53ca","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EWith every batch added into the source table \u003Cstrong\u003E(WEBLOG\u003C/strong\u003E), changes to the data returned by the query shown above are visible.\u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_11":{"id":"code-snippet-c8239ddedf","fileName":"","language":"","codeSnippet":"SELECT COUNT(*),AVG(TIME_TO_LOAD_MS)\nFROM MV_TIME_ON_SITE\nWHERE PAGE_ID=100000;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_16":{"id":"blog-text-fa76733b95","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003ETo prove that the materialized view returns the same results as a query against the source table, let’s run the statement below that will show the result from both the source table and the \u003Cstrong\u003EMV\u003C/strong\u003E at the same time. \u003C/span\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_12":{"id":"code-snippet-839d449928","fileName":"","language":"","codeSnippet":"SELECT 'WEBLOG', COUNT(*),AVG(TIME_TO_LOAD_MS)\nFROM WEBLOG\nWHERE PAGE_ID=100000\nUNION ALL\nSELECT 'MV_TIME_TO_LOAD',COUNT(*),AVG(TIME_TO_LOAD_MS)\nFROM MV_TIME_TO_LOAD\nWHERE PAGE_ID=100000;","multiLine":true,":type":"snowflake-site/components/code-snippet"},"title_v2_5":{"id":"blog-title-9ee281d942","propertiesId":"conclusion","type":"heading3","lines":["Conclusion"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_17":{"id":"blog-text-9e137ab1a2","text":"\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EUsing materialized views solves a core performance issue that occurs when accessing data in a large table through different sets of attributes. The improvements are exponential and don’t require spending time on building additional maintenance tasks, including \u003Cstrong\u003EMV\u003C/strong\u003E refresh jobs. \u003C/span\u003E\u003C/p\u003E\u003Cp\u003E\u003Cspan style=\"font-weight: 400;\"\u003EThere are many more viable use cases for materialized views, including materialized view projections and selections that enable frequently filtering queried subsets from a large source table. Support for single table aggregations can also replace manual processes for computing aggregations for one or many levels. Materialized views even supports popular functions like \u003Cstrong\u003EFLATTEN\u003C/strong\u003E or \u003Cstrong\u003ESAMPLING\u003C/strong\u003E. L\u003C/span\u003E\u003Cspan style=\"font-weight: 400;\"\u003Eearn more about \u003Ca href=\"https://www.snowflake.com/blog/automatic-clustering-materialized-views-automatic-maintenance-in-snowflake/\"\u003Ematerialized views\u003C/a\u003E and the \u003C/span\u003E\u003Ca href=\"https://docs.snowflake.net/manuals/user-guide/tables-auto-reclustering.html\"\u003E\u003Cspan style=\"font-weight: 400;\"\u003ESnowflake auto-clustering\u003C/span\u003E\u003C/a\u003E\u003Cspan style=\"font-weight: 400;\"\u003E service.\u003C/span\u003E\u003C/p\u003E\u003Cp class=\"has-text-align-center\"\u003E\u003Ca class=\"btn\" href=\"https://www.snowflake.com/snowflake-blog-subscription/\"\u003Esubscribe to the snowflake blog\u003C/a\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text_1","title_v2_1","blog_text_2","code_snippet_1","blog_text_3","title_v2_2","blog_text_4","code_snippet_2","blog_text_5","code_snippet_3","blog_text_6","code_snippet_4","blog_text_7","title_v2_3","blog_text_8","code_snippet_5","blog_text_9","code_snippet_6","blog_text_10","code_snippet_7","blog_text_11","code_snippet_8","blog_text_12","title_v2_4","blog_text_13","code_snippet_9","blog_text_14","code_snippet_10","blog_text_15","code_snippet_11","blog_text_16","code_snippet_12","title_v2_5","blog_text_17"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-f37a69d5e4",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{"author_chip":{"id":"author-chip-c5c418c18f","title":{"id":"title","type":"heading2","lines":["Learn more about the authors"],":type":"snowflake-site/components/title-v2"},"authors":[{"authorImage":{"id":"image-35221c990b","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c9f37f11-71b9-4eb5-8b7e-6e5af040e289/robert.jpg?quality=85&preferwebp=true","alt":"Robert Fehrmann","lazyEnabled":true,"height":"600","width":"662",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-9e4a04d18b","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/robert-fehrmann/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Robert Fehrmann"},"authorTitle":"Field Chief Technology 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