Sateesh Pabbathi’s Post

📊 Looking to Optimize Your Azure Synapse Analytics Serverless SQL Pool? 🚀 Curious about leveraging an efficient query for generating a date dimension table on-the-fly, especially for scenarios where storing data is impractical? DECLARE @start_date DATE = '2014-01-01'; DECLARE @end_date DATE = CURRENT_TIMESTAMP; WITH date_dimension AS (   SELECT      @start_date AS date,     YEAR(@start_date) AS year,     MONTH(@start_date) AS month,     DATENAME(WEEKDAY, @start_date) AS day_of_week,     DAY(@start_date) AS day_of_month,     DATEPART(QUARTER, @start_date) AS quarter   UNION ALL   SELECT      DATEADD(DAY, 1, date),     YEAR(DATEADD(DAY, 1, date)),     MONTH(DATEADD(DAY, 1, date)),     DATENAME(WEEKDAY, DATEADD(DAY, 1, date)),     DAY(DATEADD(DAY, 1, date)),     DATEPART(QUARTER, DATEADD(DAY, 1, date))   FROM      date_dimension   WHERE      date < @end_date ) SELECT * FROM date_dimension; ✨ Key Benefits for Serverless SQL Pool: 1️⃣ Efficient Resource Utilization: Generate dynamic date dimensions without data storage overhead, optimizing costs. 2️⃣ Scalability on Demand: Scale analytics workloads seamlessly based on demand, ensuring efficient resource allocation. 3️⃣ Simplified Maintenance: Streamline data engineering workflows with a concise query, reducing administrative complexity. #AzureSynapse #ServerlessSQL #DataAnalytics #CloudComputing #DynamicData #CostOptimization Curious to explore further? Share your insights below! 🔍🚀

🚀 Azure Synapse Analytics: Dedicated vs. Serverless SQL Pools for Data Engineering 🚀 As a data engineer, choosing the right tools for your data processing needs is crucial. Azure Synapse Analytics offers two powerful SQL pool options: Dedicated and Serverless. Both have their unique strengths, and understanding when to use each can make a big difference in your data workflows. Let's dive in! 🌊 🔹 Dedicated SQL Pools: 1️⃣ Performance and Scalability: Ideal for large-scale data warehousing, dedicated SQL pools provide consistent, high-performance data processing. They can handle massive datasets with ease, thanks to their dedicated resources. 2️⃣ Predictable Costs: With dedicated pools, you pay for reserved resources, giving you predictable billing. This is perfect for scenarios with steady, high-demand data processing. 3️⃣ Advanced Optimization: Offers features like materialized views, result set caching, and workload management to optimize query performance and resource utilization. Use Case: If you have a large, consistent workload with predictable query patterns and require advanced performance tuning, dedicated SQL pools are your go-to solution. 🔹 Serverless SQL Pools: 1️⃣ On-Demand Querying: Serverless SQL pools allow you to query data on-demand without the need to provision resources upfront. This is great for ad-hoc analysis and exploratory data processing. 2️⃣ Cost Efficiency: You only pay for the data processed by your queries, making it highly cost-effective for intermittent or unpredictable workloads. 3️⃣ Flexibility: Ideal for quick insights, serverless SQL pools can query data directly from your data lake, supporting a wide range of data formats (CSV, Parquet, JSON, etc.). Use Case: When you need flexibility, cost efficiency, and the ability to run quick, ad-hoc queries on data stored in your data lake, serverless SQL pools are the perfect choice. 🔄 Combining Both: For many data engineering projects, a hybrid approach can be the most effective. Use dedicated SQL pools for heavy, consistent workloads and serverless SQL pools for ad-hoc querying and exploratory analysis. In Summary: Choosing between dedicated and serverless SQL pools depends on your specific data needs. Understanding the strengths of each can help you design efficient, scalable, and cost-effective data solutions. Let's harness the power of Azure Synapse Analytics to drive innovation and insights in our data projects! 💡🔍 #AzureSynapse #DataEngineering #SQLPools #BigData #CloudComputing #DataAnalytics #DataScience #Azure

🚀 Azure Synapse Analytics: Dedicated vs. Serverless SQL Pools for Data Engineering 🚀 As a data engineer, choosing the right tools for your data processing needs is crucial. Azure Synapse Analytics offers two powerful SQL pool options: Dedicated and Serverless. Both have their unique strengths, and understanding when to use each can make a big difference in your data workflows. Let's dive in! 🌊 🔹 Dedicated SQL Pools: 1️⃣ Performance and Scalability: Ideal for large-scale data warehousing, dedicated SQL pools provide consistent, high-performance data processing. They can handle massive datasets with ease, thanks to their dedicated resources. 2️⃣ Predictable Costs: With dedicated pools, you pay for reserved resources, giving you predictable billing. This is perfect for scenarios with steady, high-demand data processing. 3️⃣ Advanced Optimization: Offers features like materialized views, result set caching, and workload management to optimize query performance and resource utilization. Use Case: If you have a large, consistent workload with predictable query patterns and require advanced performance tuning, dedicated SQL pools are your go-to solution. 🔹 Serverless SQL Pools: 1️⃣ On-Demand Querying: Serverless SQL pools allow you to query data on-demand without the need to provision resources upfront. This is great for ad-hoc analysis and exploratory data processing. 2️⃣ Cost Efficiency: You only pay for the data processed by your queries, making it highly cost-effective for intermittent or unpredictable workloads. 3️⃣ Flexibility: Ideal for quick insights, serverless SQL pools can query data directly from your data lake, supporting a wide range of data formats (CSV, Parquet, JSON, etc.). Use Case: When you need flexibility, cost efficiency, and the ability to run quick, ad-hoc queries on data stored in your data lake, serverless SQL pools are the perfect choice. 🔄 Combining Both: For many data engineering projects, a hybrid approach can be the most effective. Use dedicated SQL pools for heavy, consistent workloads and serverless SQL pools for ad-hoc querying and exploratory analysis. In Summary: Choosing between dedicated and serverless SQL pools depends on your specific data needs. Understanding the strengths of each can help you design efficient, scalable, and cost-effective data solutions. Let's harness the power of Azure Synapse Analytics to drive innovation and insights in our data projects! 💡🔍 #AzureSynapse #DataEngineering #SQLPools #BigData #CloudComputing #DataAnalytics #DataScience #Azure

Check this out 👇 This blog walks through how customers can use a little-known #PowerBI feature called #AutomaticAggregations to achieve sub-second BI in Power BI without #scalability issues 👏

Check out this info on how to get sub-second performance on PowerBI when querying Databricks.

#GuessWhoIsBack .. Looking for a way to get sub-second Power BI performance without sacrificing on scalability? Check out Mine and Andrey Mirskiy new Blog post on how you can use Azure Databricks SQL + Power BI Automatic Aggregations to speed up your BI queries with AI-powered Caching!  https://lnkd.in/ezGfVt_P Thanks to amazing Isaac Gritz for all your support on this one !! If you are looking for Best Practices Templates between Databricks and Power BI then do follow our git repo :https://lnkd.in/e_3qtuti #Databricks #PowerBI #AutomatedAggregation

There are reasons why PowerBI popular and Fabric (data platform) is built around it to leverage its widespread adoption. I hope Power BI continues to thrive, offering variety and strong ecosystem support.

🚀 **Unlocking New Efficiencies in Databricks SQL with Lateral Column Alias Support** In the ever-evolving landscape of data analytics and cloud technologies, it's the seemingly small innovations that often lead to significant leaps in productivity and efficiency. Much like how a cup of coffee brings clarity and focus to our day, despite its debated health effects, some features in our technological toolkit become indispensable for their utility and impact. One such feature in Databricks SQL that's stirring excitement among data professionals is the Lateral Column Alias Support. Consider the challenge of analyzing sales data to extract insights not just from the raw numbers, but also from computed metrics on the fly. Before the introduction of Lateral Column Alias Support, our approach would have been more cumbersome: Before: SELECT sales_data,     (SELECT avg(sales) FROM sales_data) as avg_sales,     sales - (SELECT avg(sales) FROM sales_data) as sales_diff FROM sales; This method works, but it lacks efficiency and elegance, especially with the repeated subquery just to calculate the difference from average sales. Now, let's see how Lateral Column Alias Support transforms this scenario: After: SELECT sales_data,     avg(sales) OVER () as avg_sales,     sales - avg_sales as sales_diff FROM sales; This sleek approach not only makes our SQL queries more readable but also significantly enhances performance by eliminating the need for repeated subqueries. It's a clear example of how Databricks SQL is not just keeping pace with the needs of data scientists and analysts but is actively facilitating a more intuitive and powerful way of working with data. Embracing Lateral Column Alias Support in Databricks SQL is like finding a new way to enjoy your coffee – it might just change your perspective on what's possible, leading to more efficient, insightful, and impactful data analysis. Let's continue to leverage these advancements, pushing the boundaries of what we can achieve with our data on Databricks. PS: LCA is fully available and enabled by default in Databricks Runtime 12.2 LTS and later, in Databricks SQL 2023.20 and above, and Apache Spark 3.4 #DatabricksSQL #DataAnalytics #Efficiency #Innovation #CloudComputing

Star Schema vs. Snowflake Schema!!! If you’re working with data warehousing, you've likely come across these two common data modeling approaches: Star Schema and Snowflake Schema. But what are they, and how do they differ? 🔍 Star Schema: Simple and Fast: The star schema is the simplest form of data warehousing schema, where the fact table (which contains metrics like sales, revenue, etc.) is connected to dimension tables (such as time, product, or customer data) in a star-shaped pattern. Key Benefit: It’s easy to understand and fast for querying, making it ideal for OLAP (Online Analytical Processing) systems where simplicity and performance are key. 🔍 Snowflake Schema: More Normalized: The snowflake schema takes the star schema one step further by normalizing dimension tables. This means dimension tables are split into sub-dimension tables to remove redundancy. The result is a snowflake-like structure. Key Benefit: Less redundancy and better storage optimization but comes with more complex queries since you’ll be joining multiple tables. 🚀 Which One Should You Use? Star Schema: Go with this when you need fast query performance and are dealing with simpler data relationships. Ideal for reporting and dashboards. Snowflake Schema: Use this when you need efficient storage and are handling complex relationships between data points. This model is ideal when you’re optimizing for space and want to reduce data redundancy. Understanding these schemas is crucial for designing effective data warehouses. Whether you prioritize query speed or storage efficiency will determine which approach works best for your project! #softwareengineering #dataengineering #bigdata #database #performance #distributedsystem #parallelcomputing #spark #interview #pyspark #databricks #AI #machinelearning #cloudcomputing #ETL #datascience #hadoopecosystem #careertips #datapipelines #scalability #clouddata #devops Sumit Mittal Zach Morris Wilson Snowflake Databricks Apache Spark

🔶 Choosing the Right ETL tools: I have mentioned some of the factors with couple of examples.. 🔹 End Requirement: Clear abstracted view on the end user requirement makes path for choosing tools. Examples- 💠 End requirement is of performing any Business intelligence or to generate the dimensional models for the visualization team for daily or weekly feed then choosing snowflake or Synapse which have integarted connectors for BI tools like PowerBI makes the work easy. 💠 If the requirement is of handling wide variety of data for Data science or Machine learning models to be implemented then choosing lakehouse solutions will be optimal. 🔹 Ease of Use: Considering the tool needs how flexible we can integrate the tool with the existing environment or in getting the desired output. Example:   💠 Snowflake provides the inbuilt connectors for Fivetran and DBT for ETL. 🔹 Performance: Data warehouses and Data lake houses has different architectures for processing, storing and Managing the data both are exceling by providing auto scalability (horizontal and vertical) , redundancy , reliability , resilience Governance . Example:    💠 Data Warehouses Snowflake follow MPP architecture where its provides the File access sharing with out moving its data .     💠 Data lake house works on principle of Data locality where the spark engine works as the compute engine. 🔹 Cost:     Cost is the major consideration for choosing any data processing tools. Different tools have different approaches of generating bills for resources utilized . Example:     💠 Databricks charges as per DBU's utilized, along with all the corresponding resources helps in building pipeline comes in to consideration.     💠 Snowflake has different T-shirt sizes of warehouses and it charges for the total active time of the cluster and along with that if there are any additional ETL or resources used in building pipeline they are explicitly charged. 🔺 There may be some of the external factors like vendor contracts , available resources for project and many more may come in to consideration. #Data #DataEngineer #ETL #ELT #WareHouse #VW #snowflake #databricks #dbt #Azure #AWS #delta #datalakehouse #datalake