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in your laptop and use Fabric only for Production", "url": "https://datamonkeysite.com/2023/09/01/develop-python-notebook-in-your-laptop-and-use-fabric-only-for-production/", "published_at": "2023-09-01T12:42:34+00:00" }, { "id": "01a0c542-4305-7036-b1bf-8de9273e66fc", "title": "Sharing Data Between OneLake and Snowflake", "url": "https://datamonkeysite.com/2023/08/25/sharing-data-between-onelake-and-snowflake/", "published_at": "2023-08-25T10:02:45+00:00" }, { "id": "01a0c542-4305-7036-b1bf-8de9281a843e", "title": "First Look at Dataflow Gen2", "url": "https://datamonkeysite.com/2023/08/18/first-look-at-dataflow-gen2/", "published_at": "2023-08-18T11:38:31+00:00" }, { "id": "01a0c542-4305-7036-b1bf-8de92839aca9", "title": "Loading files from a folder to Fabric DWH using data Factory pipeline.", "url": "https://datamonkeysite.com/2023/08/11/loading-files-from-a-folder-to-fabric-dwh-using-data-factory-pipeline/", "published_at": "2023-08-11T02:18:15+00:00" }, { "id": "01a0c542-4305-7036-b1bf-8de92843d20d", "title": "First Look at Fabric F2 ", "url": "https://datamonkeysite.com/2023/08/05/first-look-at-fabric-f2/", "published_at": "2023-08-05T10:42:44+00:00" }, { "id": "01a0c542-4305-7036-b1bf-8de9287a2d73", "title": "Fabric Preview Pricing Misconception", "url": "https://datamonkeysite.com/2023/07/26/fabric-preview-pricing-misconception/", "published_at": "2023-07-26T12:46:32+00:00" }, { "id": "01a0c54b-2450-7333-adc0-19504206cd54", "title": "PowerBI Direct Lake misconception", "url": "https://datamonkeysite.com/2023/07/09/powerbi-direct-lake-misconception/", "published_at": "2023-07-09T09:17:10+00:00" }, { "id": "01a0c54b-2450-7333-adc0-195042a6c561", "title": "Fabric as a OS for analytics ", "url": "https://datamonkeysite.com/2023/06/29/fabric-as-a-os-for-analytics/", "published_at": "2023-06-29T06:04:48+00:00" }, { "id": "01a0c54b-2450-7333-adc0-195042f15a2d", "title": "Save Fabric Delta Tables as DuckDB file", "url": "https://datamonkeysite.com/2023/06/26/save-fabric-delta-tables-as-duckdb-file/", "published_at": "2023-06-26T00:25:46+00:00" }, { "id": "01a0c54b-2450-7333-adc0-195043b33a79", "title": "What is the Fastest Engine to sort small Data in a Fabric Notebook?", "url": "https://datamonkeysite.com/2023/06/19/what-is-the-fastest-engine-to-sort-small-data-in-a-fabric-notebook/", "published_at": "2023-06-19T05:36:57+00:00" }, { "id": "01a0c54b-2450-7333-adc0-195043c1b040", "title": "ACID Transaction in Fabric ?", "url": "https://datamonkeysite.com/2023/06/15/acid-transaction-in-fabric/", "published_at": "2023-06-15T12:48:58+00:00" }, { "id": "01a0c54b-2450-7333-adc0-195044a0ee1c", "title": "Create Delta Table in Azure Storage using Python and serve it with Direct Lake Mode in Fabric.", "url": "https://datamonkeysite.com/2023/06/13/create-delta-table-in-azure-storage-using-python-and-serve-it-with-direct-lake-mode-in-fabric/", "published_at": "2023-06-13T06:09:05+00:00" }, { "id": "01a0c54b-2450-7333-adc0-195044c2da11", "title": "Importing Delta table from OneLake to PowerBI", "url": "https://datamonkeysite.com/2023/06/06/importing-delta-table-from-onelake-to-powerbi/", "published_at": "2023-06-06T03:36:21+00:00" }, { "id": "01a0c54b-2450-7333-adc0-195045a924ae", "title": "First Look at Fabric Serverless Spark", "url": "https://datamonkeysite.com/2023/05/30/first-look-at-fabric-serverless-spark/", "published_at": "2023-05-30T13:14:26+00:00" }, { "id": "01a0c54b-2450-7333-adc0-195045e0d16d", "title": "First impression of Microsoft Fabric", "url": "https://datamonkeysite.com/2023/05/27/first-impression-of-microsoft-fabric/", "published_at": "2023-05-27T06:22:53+00:00" }, { "id": "01a0c54b-2450-7333-adc0-195046325504", "title": "Databend and the rise of Data warehouse as a code", "url": "https://datamonkeysite.com/2023/05/22/databend-and-the-rise-of-data-warehouse-as-a-code/", "published_at": "2023-05-22T11:33:20+00:00" }, { "id": "01a0c553-862f-71c3-a3e5-7960ad008ec0", "title": "Optimize BigQuery cost by creating independent sort order for the same table", "url": "https://datamonkeysite.com/2023/04/21/optimize-bigquery-cost-by-creating-independent-sort-order-for-the-same-table/", "published_at": "2023-04-21T11:21:38+00:00" }, { "id": "01a0c553-8631-70e2-a6bb-b8d0691e236e", "title": "The Unreasonable Effectiveness of Snowflake SQL Engine", "url": "https://datamonkeysite.com/2023/04/10/the-unreasonable-effectiveness-of-snowflake-sql-engine/", "published_at": "2023-04-10T11:53:21+00:00" }, { "id": "01a0c553-8631-70e2-a6bb-b8d06a094212", "title": "First Look at Tableau Hyper", "url": "https://datamonkeysite.com/2023/03/22/first-look-at-tableau-hyper/", "published_at": "2023-03-22T04:59:15+00:00" }, { "id": "01a0c553-8631-70e2-a6bb-b8d06a6c3080", "title": "Benchmarking , Snowflake, Databricks , Synapse , BigQuery, Redshift , Trino , DuckDB and Hyper using TPCH-SF100", "url": "https://datamonkeysite.com/2023/03/09/benchmarking-snowflake-databricks-synapse-bigquery-and-duckdb-using-tpch-sf100/", "published_at": "2023-03-09T04:36:25+00:00" }, { "id": "01a0c553-8631-70e2-a6bb-b8d06b02b3eb", "title": "Implementing a Poor Man’s Lakehouse in Azure", "url": "https://datamonkeysite.com/2023/02/23/implementing-a-poor-mans-lakehouse-in-azure/", "published_at": "2023-02-23T04:44:28+00:00" }, { "id": "01a0c553-8631-70e2-a6bb-b8d06bb0f7d7", "title": "Apache Spark Benchmark for TPCH-SF10", "url": "https://datamonkeysite.com/2023/02/12/apache-spark-benchmark-for-tpch-sf10/", "published_at": "2023-02-12T06:20:26+00:00" }, { "id": "01a0c553-8631-70e2-a6bb-b8d06c38d31a", "title": "First Look at Open Table Format", "url": "https://datamonkeysite.com/2023/02/09/first-look-at-open-table-format/", "published_at": "2023-02-09T10:18:04+00:00" }, { "id": "01a0c553-8631-70e2-a6bb-b8d06d20e720", "title": "Querying Azure storage using DuckDB", "url": "https://datamonkeysite.com/2023/01/27/querying-azure-storage-using-duckdb/", "published_at": "2023-01-27T08:39:53+00:00" }, { "id": "01a0c553-8631-70e2-a6bb-b8d06d707055", "title": "Using Apache Arrow Dataset to compact old partitions", "url": "https://datamonkeysite.com/2022/12/18/using-apache-arrow-dataset-to-compact-old-partitions/", "published_at": "2022-12-18T07:24:10+00:00" }, { "id": "01a0c553-8631-70e2-a6bb-b8d06d8ecdb0", "title": "PowerBI Query plan when using Top N filter", "url": "https://datamonkeysite.com/2022/10/21/powerbi-query-plan-when-using-top-n-filter/", "published_at": "2022-10-21T10:59:56+00:00" }, { "id": "01a0c55b-fc6e-721b-8586-1acdc3b3fa08", "title": "Multi fact support in DAX and Malloy", "url": "https://datamonkeysite.com/2022/09/13/multi-fact-support-in-dax-and-malloy/", "published_at": "2022-09-13T06:55:56+00:00" }, { "id": "01a0c55b-fc6e-721b-8586-1acdc4567ca2", "title": "Running a Serverless DuckDB on Google Cloud", "url": "https://datamonkeysite.com/2022/08/27/running-a-serverless-duckdb-on-google-cloud/", "published_at": "2022-08-27T08:20:56+00:00" }, { "id": "01a0c55b-fc6e-721b-8586-1acdc53beabf", "title": "Poor Man’s lakehouse using Cloud Storage, Delta lake and DuckDB", "url": "https://datamonkeysite.com/2022/08/21/poor-mans-lakehouse-using-cloud-storage-delta-lake-and-duckdb/", "published_at": "2022-08-21T09:38:01+00:00" }, { "id": "01a0c55b-fc6e-721b-8586-1acdc5fbcc5f", "title": "Query Performance in Vertipaq vs DuckDB", "url": "https://datamonkeysite.com/2022/08/19/query-performance-in-vertipaq-vs-duckdb/", "published_at": "2022-08-19T07:20:17+00:00" }, { "id": "01a0c55b-fc6e-721b-8586-1acdc6f39861", "title": "Expanded Table Behavior in DAX and Malloy", "url": "https://datamonkeysite.com/2022/08/16/expanded-table-behavior-in-dax-and-malloy/", "published_at": "2022-08-16T04:29:34+00:00" }, { "id": "01a0c55b-fc6e-721b-8586-1acdc7a05374", "title": "Aggregate 100 GB using PowerQuery and DuckDB", "url": "https://datamonkeysite.com/2022/08/08/aggregate-100-gb-using-powerquery-and-duckdb/", "published_at": "2022-08-08T05:47:23+00:00" }, { "id": "01a0c55b-fc6f-7048-8284-e8c80f733a24", "title": "How an Open Source PowerBI Desktop May Look like", "url": "https://datamonkeysite.com/2022/07/31/how-an-open-source-powerbi-desktop-may-look-like/", "published_at": "2022-07-31T05:38:56+00:00" }, { "id": "01a0c55b-fc6f-7048-8284-e8c80f9d4161", "title": "Building Complex Data Model using Nested Data in Malloy", "url": "https://datamonkeysite.com/2022/07/26/building-complex-data-model-using-nested-data-in-malloy/", "published_at": "2022-07-26T09:53:22+00:00" }, { "id": "01a0c55b-fc6f-7048-8284-e8c8100a3563", "title": "First Look at Google Malloy", "url": "https://datamonkeysite.com/2022/07/22/first-look-at-google-malloy/", "published_at": "2022-07-22T10:51:15+00:00" }, { "id": "01a0c55b-fc6f-7048-8284-e8c81084b979", "title": "Advance Geospatial analysis using location Parameter with Streamlit", "url": "https://datamonkeysite.com/2022/07/07/advance-geospatial-analysis-using-location-parameter-with-streamlit/", "published_at": "2022-07-07T09:00:00+00:00" } ] posts Claim your blog
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