sanjeev kumar

The pg_wal directory in PostgreSQL is essential for maintaining data durability and recovery through Write-Ahead Logging (WAL). However, excessive growth in this directory can consume valuable disk space and, if unmanaged, may even lead to outages due to insufficient storage. #PostgreSQLPerformance @MinervaDB https://zurl.co/QXpp

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Quiz Time # 538 | 19-Nov-2024 | MS SQL DBA   A counter is the definition for how many piece of performance data 1.                  Single 2.                  Double 3.                  Triple 4.                  Multiple comment your answer Follow me and #dbaasservice for more update DBA as a Service: DBA as a Service (DBaaS) is a modern approach to database management where your organization can leverage 1. Dedicated expertise without the need for an in-house DBA. 2. Expand DBA support without increasing headcounts 3. Eliminate DBA recruiting and training cost. #sqldba #quiz #australia #sqlserver #sql #database #azure #microsoft #tips #careergoals   

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Snowflake question: on Netezza, you had to be connected to a database to create an object, eg a procedure. on Snowflake, you can compile a proc to a database you are not currently using if the procedure name is qualified with DB and schema. is it possible to prevent this and reciprocate Netezza behaviour which I believe was safer?

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#MySQL's Performance Schema is a powerful tool for monitoring and analyzing query execution performance. By delving into query execution details, you can identify bottlenecks, resource-intensive operations, and optimize for better performance. #MySQLPerformance @MinervaDB #dba https://zurl.co/P3Oh

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SQL DBA with MarkLogic and Netezza -Branchville, NJ (Hybrid)-Locals needed   An L3 role typically involves advanced-level responsibilities, including resolving complex issues, overseeing system stability, and providing expert guidance. Here's a breakdown based on specifications for SQL (70%), MarkLogic (20%),  1. SQL   Database Design and Optimization: Designing efficient database schemas, indexing strategies, and query optimization to improve performance.   Performance Tuning: Monitoring and optimizing the performance of SQL queries, stored procedures, and overall database health.   Troubleshooting: Handling escalated issues from L2 support, including complex SQL errors, deadlocks, and data inconsistencies.   Data Integrity and Security: Implementing security measures, such as access controls and encryption, ensuring data integrity and compliance with standards.   Backup and Recovery: Managing and maintaining database backups, ensuring disaster recovery processes are in place.   Automation: Creating and maintaining scripts for database tasks, including backups, monitoring, and alerts.   Collaboration: Working with development teams to optimize database interactions and provide guidance on best practices.     2. MarkLogic   Database Administration: Managing the MarkLogic NoSQL database, including installation, configuration, and upgrading.   Query Optimization: Optimizing XQuery and search performance in MarkLogic, ensuring efficient data retrieval.   Data Management: Managing document-based data structures, indexing, and retrieval, ensuring the system handles large-scale data efficiently.   Troubleshooting and Maintenance: Handling escalated MarkLogic-specific issues, including performance bottlenecks and system failures.   Security: Managing security features like user roles, permissions, and encryption in MarkLogic.     3. Netezza   System Administration: Administering the Netezza appliance, including system health checks, upgrades, and maintenance.   Performance Tuning: Optimizing Netezza-specific SQL queries, particularly those involving large datasets.   Troubleshooting: Handling escalated issues specific to the Netezza environment, such as performance lags or database errors.   Data Load Management: Overseeing data load processes to ensure efficient ETL operations.   Can you please share me the resumes to [email protected]

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Role: AWS Data Architect Location: Tampa FL - Hybrid Position Overview: The AWS Databricks Solution Architect/Lead will be responsible for designing, implementing, and optimizing data solutions using AWS and Databricks platforms. This role requires a deep understanding of cloud architecture, data engineering, and analytics to deliver scalable, high-performance solutions. The ideal candidate will have a proven track record of successful project leadership, technical expertise, and the ability to translate complex business requirements into effective technical solutions. Key Responsibilities: ◾ Architecting Solutions: Design and implement scalable, reliable, and high-performance data solutions on AWS using Databricks, ensuring alignment with business objectives and technical requirements. ◾ Technical Leadership: Lead and mentor a team of data engineers, analysts, and architects. Provide guidance on best practices, architectural decisions, and the use of Databricks and AWS services. ◾ Business Engagement: Collaborate with stakeholders to understand their data needs and business goals. Translate these needs into technical specifications and solutions. ◾ Solution Design: Develop end-to-end data pipelines, data warehousing, and analytics solutions leveraging Databricks and AWS services such as S3, Redshift, Glue, and Lambda. ◾ Documentation: Create and maintain detailed architectural diagrams, design documents, and implementation plans. Ensure comprehensive documentation of solutions and processes. ◾ Collaboration: Work closely with cross-functional teams including data scientists, business analysts, and developers to ensure seamless integration of data solutions. Qualifications: ◾ Experience: 7+ years of experience in data engineering, cloud architecture, or related fields with a focus on AWS and Databricks. Proven track record of successful project delivery and technical leadership. Technical Skills: ◾ In-depth knowledge of AWS services, including but not limited to S3, EC2, RDS, Redshift, Glue, and Lambda. ◾ Extensive experience with Databricks, including Spark SQL, MLlib, and Delta Lake. ◾ Strong understanding of data modeling, ETL processes, and data warehousing concepts. ◾ Proficiency in programming languages such as Python, Scala, or SQL. ◾ Certifications: AWS Certified Solutions Architect (Associate or Professional) and Databricks certifications are highly desirable. ◾ Communication: Excellent verbal and written communication skills with the ability to convey complex technical concepts to non-technical stakeholders. ◾ Problem-Solving: Strong analytical and problem-solving skills with a creative and innovative approach to tackling challenges. ◾ Leadership: Proven experience leading teams and projects, with a strong ability to mentor and guide junior team members. Ankur Awale || US IT Recruiter Nityo Infotech Corp [email protected] #AWS #DataArchitect #Data_Architect #S3 #Glue #RDS #EC2 #Databricks #C2C #CorptoCorp

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https://zurl.co/OBRt Understanding Index Selection Mechanics in PostgreSQL #PostgreSQL #DBA #PostgreSQLPerformance #SQLTips #PostgreSQLConsulting

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---SQL Server DBA Performance Tuning First, we try to find expensive queries as 99% of performance issues are because of bad queries. Once we know bad queries we try to find expensive operators into them, like: Lazy spool Hash match Keylookup Index Suggestion Partition Elimination BAD Views Minimizing sub queries Lazy spool: Statement: Lazy spool is most expensive operator among all, which happens because of duplicate aggregation. Resolution: We can remove duplicate aggregation by using temporary tables, cte, etc. Hash match:  Statement: Which is always because of unsorted data. Which means either missing index or indexes are not properly utilized. Resolution: We can address this by adding new index or fixing query to utilize existing index Keylookup:  Statement: Keylookup just indicate, that index is missing some data. Resolution: We can easily resolve it by adding missing column to index key. Exception: There is a catch as, we don't have privilege to alter index all the time. So, sometimes, we cannot avoid keylookup. Index Suggestion: Index key columns --> part of where clause or joins. Index include columns --> part of only selection. Partition Elimination:  we check if partition is getting eliminated in query or not. As sometimes even tables have partitions still query go for scan instead partition elimination. A lot of times, I have seen, developer use different datatype in storedprocedure parameters which don't match to actual column. And end up in no partition elimination. Two reasons why query is not eliminating partition: 1. Data type mismatch or type casting. 2. Partition key column is not part of where clause. BAD Views:  Statement: Views sometimes becomes bad when developer don't alter them instead, they join same table to view which is already part of view to get some extra data. Resolution: By writing new view or alter existing view to get extra columns. Minimizing sub queries:  Statement: We can minimize sub queries, if subqueries belong to same table to get different columns. Resolution: By using Cross Apply or Outer Apply.

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In #PostgreSQL, index selection is a key part of the query planner, which determines the most efficient execution plan for queries. This process optimizes performance by selecting appropriate indexes based on query structure, schema design, and data statistics. #PostgreSQLPerformance @MinervaDB #dba https://zurl.co/zodQ

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In #PostgreSQL, index selection is a key part of the query planner, which determines the most efficient execution plan for queries. This process optimizes performance by selecting appropriate indexes based on query structure, schema design, and data statistics. #PostgreSQLPerformance @MinervaDB #dba https://zurl.co/zodQ

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Why PostgreSQL is one of the best databases in the world: 1. Open Source & Cost-Effective – No licensing fees, backed by a strong community. 2. Advanced Features & Extensibility – ACID compliance, JSONB support, and a wide range of custom functions and extensions. 3. Performance & Scalability – Handles complex queries easily, offering vertical and horizontal scalability. 4. Security & Data Integrity—This service ensures high security with advanced features like SSL and Row-Level Security (RLS). Postgres truly stands out for developers, businesses, and enterprises alike! #PostgreSQL #Database #Tech #DataScience #Scalability NAYAN SQL,Power BI ,Tableau GHOSH Rishabh Mishra

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