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For collaboration, brand promotion and tech event speaking contact at: azurelibblog@gmail.com Hi, I'm Deepak Goyal, a certified Big data, and Azure Cloud Solution Architect. I have 15+ Years of experience in the IT industry and 12+ Years of experience in Big data world. I was among the few who has worked on Hadoop Big data analytics (since the Year 2011) before the popular advancement and adoption of Public cloud providers like AWS, Microsoft Azure, or Google Cloud Platform (GCP) I help businesses to define the data-driven architecture and make robust data platform over the cloud to scale up their business. Writing about Microsoft Azure technologies is one of my favorite works outside the office. I help people to understand cloud concepts and technologies like Azure Data Factory, Azure DataBricks, Apache Spark, Azure Synapse Analytics, Azure Key Vault, Encryption Decryption, Azure Blob Storage, Azure monitor, logging, Snowflake cloud data warehouse, Fivetran and many more complex tools and technologies. I am a famous Azure blogger, my blog ranks number 1 on google search for more than 100+ keywords. The aim of this blog website is to share my expertise and experience as SME, to help people, learn by tutorials bundled with my personal experience, crack interviews, get their dream job and elevate themselves to the next level without paying anything. I am a Youtuber too having more than 2K+ subscribers, who make videos on technical content like Hadoop, Mapreduce, pySpark, python, Snowflake, and many more. Here on Linkedin as well, I talk and write about Azure Data engineer, Azure certifications like Az-900, DP-203 study guide, DP-900, Az-104, Az-305, also solves people's technical queries and challenges. In a nutshell, I am someone who spends a lot of his time working and talking about technology. If you ever wonder about my work then you MUST check out ๐ - azurelib.com - check my Youtube channel at @ItSkillsUpgrade Feel free to hit me up in the LinkedIn DM, or simply send "Hi" to azurelibblog@gmail.com Thank you, Deepak Goyal **DISCLAIMER: All activity on this platform represents solely my own opinions and is not in any way, shape, or form, a representation of my current or past employers **
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๐๐จ๐ฐ ๐ญ๐จ ๐ข๐ฆ๐ฉ๐ซ๐จ๐ฏ๐ ๐๐๐ญ๐๐๐๐ฌ๐ ๐ฉ๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐? Here are some of the top ways to improve database performance: 1. Indexing Create the right indexes based on query patterns to speed up data retrieval. 2. Materialized Views Store pre-computed query results for quick access, reducing the need to process complex queries repeatedly. 3. Vertical Scaling Increase the capacity of the hashtag#database server by adding more CPU, RAM, or storage. 4. Denormalization Reduce complex joins by restructuring data, which can improve query performance. 5. Database Caching Store frequently accessed data in a faster storage layer to reduce load on the database. 6. Replication Create copies of the primary database on different servers to distribute read load and enhance availability. 7. Sharding Divide the database into smaller, manageable pieces, or shards, to distribute load and improve performance. 8. Partitioning Split large tables into smaller, more manageable pieces to improve query performance and maintenance. 9. Query Optimization Rewrite and fine-tune queries to execute more efficiently. 10. Use of Appropriate Data Types Select the most efficient data types for each column to save space and speed up processing. 11. Limiting Indexes Avoid excessive indexing, which can slow down write operations; use indexes judiciously. 12. Archiving Old Data Move infrequently accessed data to an archive to keep the active database smaller and faster. Post credit: Arslan Ahmad
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