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Databases
Amazon
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232
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Amazon CloudWatch Database Insights applied in real scenarios
- AWS introduced Amazon CloudWatch Database Insights in December 2024, tailored for Amazon Aurora and Amazon RDS across multiple databases.
- CloudWatch Database Insights aims to enhance database observability for DevOps engineers and database administrators, facilitating troubleshooting.
- The tool provides two modes of operation- Standard Mode and Advanced Mode, with Advanced Mode offering additional features.
- Enabling CloudWatch Database Insights involves activating the Advanced Mode, which incurs a cost.
- Accessing CloudWatch Database Insights through the Amazon CloudWatch console allows for efficient troubleshooting.
- Demo #1 showcases resolving a query performance issue by identifying the need for an index and optimizing the query execution plan.
- Demo #2 illustrates addressing storage capacity limits during database migration using CloudWatch Database Insights.
- Demo #3 exemplifies mitigating database restarts due to out-of-memory issues using instance size changes and failover.
- By leveraging CloudWatch Database Insights, users can effectively troubleshoot database issues and optimize performance.
- Authors include Nirupam Datta, Ryan Moore, and Cade Kettner, experts in AWS database services providing technical assistance and guidance.
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Cloudblog
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105
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Cool stuff customers built, May edition: Visual scouts, racing agents, agile ads & more
- AI and cloud technology are transforming industries globally, as highlighted by customers utilizing Google Cloud for innovative projects.
- Notable projects include Lowe's Visual Scout product recommendation engine, Formula E's AI-powered Driver Agent for race insights, and Wealth.com's conversational tool Ester AI for financial planning.
- Additionally, Yassir consolidated its data infrastructure with Google Cloud, SIGNAL IDUNA enhanced customer service with an AI knowledge assistant, and Nuro improved AI-driven insights with AlloyDB.
- Mars Wrigley utilized BigQuery and Cortex Framework for agile media experiments, showcasing the impact of data-driven marketing strategies.
- These projects demonstrate how technology like AI, cloud computing, and data analytics are reshaping businesses and customer experiences across various industries.
- From enhancing ecommerce experiences to democratizing access to race insights, these initiatives showcase the diverse applications of cutting-edge technology.
- By leveraging Google Cloud services, companies are optimizing operations, improving customer satisfaction, and driving innovation in their respective sectors.
- These real-world examples underscore the importance of technology partnerships and the potential for AI to augment human expertise and decision-making in complex domains.
- As more industry leaders and startups embrace Google Cloud technologies, the impact on business processes, customer interactions, and data-driven insights is set to expand further.
- The showcased projects not only highlight the capabilities of Google Cloud but also emphasize the significance of data utilization and AI integration for driving business growth and operational efficiencies.
- Collaborations between companies and Google Cloud are enabling transformative projects that enhance user experiences, optimize processes, and propel technological advancements in various sectors.
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Cloudblog
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39
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Democratizing database observability with AI-assisted troubleshooting
- Organizations are adopting DevOps practices, requiring application developers to manage databases without specialized expertise, leading to delays in development.
- Google Cloud introduced AI-assisted troubleshooting for Cloud SQL and AlloyDB, enabling developers to efficiently manage and troubleshoot databases.
- AI-assisted troubleshooting predicts and resolves potential database performance issues, provides actionable recommendations, and simplifies query optimization.
- This new feature leverages generative AI and machine learning to enhance database operations, address hotspots, and optimize performance, promoting independence and efficiency among developers.
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2 Likes
Dev
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184
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How to Retrieve Hierarchical Flex Values in Oracle EBS?
- Hierarchical queries in Oracle EBS help in understanding parent-child relationships within flex values for effective reporting.
- Using tables like FND_FLEX_VALUE_NORM_HIERARCHY and FND_FLEX_VALUE_CHILDREN_V aids in constructing queries for hierarchical data retrieval.
- Example query using FND_FLEX_VALUE_NORM_HIERARCHY showcases retrieving flex values with their hierarchy, displaying depth and paths.
- Alternatively, the query utilizing FND_FLEX_VALUE_CHILDREN_V simplifies hierarchy retrieval, but may not list top parents.
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Dev
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435
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Getting Started with Simple Data Integrator (SDI): Direct Connection Setup
- Simple Data Integrator (SDI) is a no-code ETL tool for connecting and transferring data between databases and systems.
- This article provides a guide on setting up a Direct Connection in SDI, using Oracle as an example.
- Steps include opening Connection Settings, filling in Connection Details like Oracle details, and testing the connection.
- SDI supports connections to various databases like SQL Server, MySQL, PostgreSQL, DB2, and ODBC-compliant databases with similar setup procedures.
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Dev
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171
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10 SQL Anti-Patterns You Must Avoid in Production
- SQL developers and data engineers often prioritize functionality over query quality, leading to various issues like long response times and poor scalability.
- Key SQL anti-patterns include N+1 Query Pattern, Wildcard Index Scans, Implicit Data Type Conversions, Scalar Subqueries in SELECT, and SELECT * in Production.
- Solutions involve optimizing queries by using joins, avoiding leading wildcards in LIKE statements, matching data types explicitly, and selecting only necessary columns.
- Other tips include using WHERE clauses in DELETE/UPDATE statements, adding indexes on foreign keys, and correctly utilizing UNION ALL instead of overusing OR.
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10 Likes
Dev
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382
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Working with JSON in PostgreSQL, MySQL & SQL Server
- SQL databases now support semi-structured data such as JSON alongside traditional relational models.
- The hybrid approach allows for storing rich nested data, adapting to evolving schemas, and joining structured and flexible data together.
- Key points covered are JSON column types, querying nested structures, indexing for performance, and cross-database examples in PostgreSQL, MySQL, and SQL Server.
- The article emphasizes the benefits of using JSON in SQL databases for various use cases such as dynamic user profiles, event logs, IoT sensor payloads, and more.
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23 Likes
Dev
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307
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Querying the Past: Temporal Tables in SQL Server and PostgreSQL
- SQL Server and PostgreSQL offer temporal tables for tracking changes over time, essential for compliance, historical reporting, and debugging.
- Temporal tables allow querying data as it was in the past without custom history-tracking logic.
- SQL Server uses system-versioned tables while PostgreSQL requires triggers and audit tables for temporal features.
- Best practices include maintaining a temporal schema, logging changes, querying past states, and following data governance practices for tracking history.
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18 Likes
Dev
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189
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How to use EXPLAIN to fix slow queries in MySQL
- EXPLAIN in MySQL is a helpful tool for diagnosing and optimizing slow queries that can impact performance.
- By using EXPLAIN before a query, developers and administrators can understand the execution plan and identify underperforming areas.
- The output of EXPLAIN can be in table format or structured JSON for more detailed information.
- Key aspects to consider when interpreting EXPLAIN results include query structure, access methods, index usage, row scanning, and unnecessary work.
- EXPLAIN helps in diagnosing performance issues like full scans, unused indexes due to pattern matching, inefficient joins, sorting, and filtering problems.
- Additional tools like MySQL Workbench, Percona Toolkit, EverSQL, and Releem can complement EXPLAIN for query analysis and optimization.
- By leveraging EXPLAIN and other tools, developers can streamline the process of identifying and resolving inefficiencies in MySQL queries.
- Using EXPLAIN regularly can aid in improving database performance, optimizing queries, and enhancing overall system efficiency.
- Be proactive in incorporating EXPLAIN into your MySQL toolkit to effectively manage databases and build high-performing queries.
- Understanding and utilizing EXPLAIN effectively can lead to significant performance enhancements and better database management.
- Empower yourself with the knowledge and tools provided by EXPLAIN to tackle slow query issues and optimize MySQL database operations.
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11 Likes
Cloudblog
3w
17
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Multi-cloud AI made easier: Aiven for AlloyDB Omni now generally available
- Aiven for AlloyDB Omni is now generally available, providing a fully managed, high-performance PostgreSQL-compatible solution for building modern, data-driven applications.
- Key benefits of Aiven for AlloyDB Omni include strong uptime SLA across major clouds, a consistent multi-cloud experience, marketplace availability, disaster recovery options, superior price/performance, and flexible licensing.
- The solution seamlessly integrates with Aiven's unified data platform, offering tools to accelerate AI initiatives, reduce management overhead, scale without limits, and streamline data pipelines.
- AlloyDB Omni delivers PostgreSQL compatibility, faster transactional workloads, accelerated analytical queries, and improved vector search operations, now available as a managed service across AWS, Azure, and Google Cloud.
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Dbi-Services
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422
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M-Files IMPACT Global Conference 2025 – Day 3
- The M-Files IMPACT Global Conference 2025 on its last day was exclusive for M-Files partners where partner-specific information and updates were shared.
- Keynote session highlighted continued support for the partner network and shared financial results for partners in 2024 and Q1 2025, focusing on networking and learning.
- Sessions on best practices for impactful demos, implementing M-Files Hubshare, and new M-Files Admin features were conducted to enhance partner capabilities.
- The conference concluded with sessions on Partner Cloud updates, developer tools, and emphasized the importance of the partner network for future collaboration.
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25 Likes
Dev
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325
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EXPLAIN It! Your Fast Track to Fixing Slow SQL
- The "EXPLAIN" command in SQL provides insights into query execution strategies, aiding in optimizing slow queries.
- It reveals details such as indexes used, table join order, scanning methods, and more.
- By analyzing the output of EXPLAIN, you can identify inefficiencies in queries and take steps to improve them.
- Two case studies are presented to illustrate how EXPLAIN can guide SQL optimization efforts.
- In Case Study 1, optimizing a simple count query involves checking indexes and ensuring efficient data retrieval.
- In Case Study 2, optimizing a multi-table join and aggregation query requires early filtering and appropriate indexing.
- Combining EXPLAIN analysis with practical optimization techniques can enhance query performance.
- Tools like Chat2DB offer AI-powered database assistance to streamline SQL optimization processes.
- Chat2DB provides features like query generation, optimization suggestions, EXPLAIN execution, and seamless database management.
- These tools empower users to enhance database efficiency and write better-performing SQL queries.
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19 Likes
Dev
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264
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Aggregate according to Time Interval — From SQL to SPL #27
- A database table stores time-series data with intervals of several seconds between each record.
- Task is to group and aggregate data every minute, summarizing data for 5 minutes each time.
- SQL implementation requires nested subqueries and multiple window functions, making the code complex.
- SPL provides direct access syntax for positions, making the code more streamlined and efficient.
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15 Likes
Dev
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268
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Why Snowflake Column-Level Masking Outshines Traditional Tokenizationi9
- Traditional tokenization is facing limitations in modern data platforms like Snowflake, leading organizations to consider column-level masking for improved security and flexibility.
- Tokenization alters data at rest and requires external systems, slowing down analytics and lacking flexibility. It's suitable for specific compliance needs but may hinder general analytics.
- Snowflake's column-level masking offers a cleaner alternative by dynamically changing data visibility based on user roles without permanently altering the original data.
- Comparing tokenization and column-level masking in Snowflake shows that the latter provides better data usability, access control, and performance with lower maintenance and higher flexibility.
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16 Likes
Dev
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414
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How to Perform Row-Wise Aggregation in DuckDB Using SQL?
- Performing row-wise aggregation in DuckDB using SQL involves filtering and aggregating data from multiple tables based on specific criteria.
- You can achieve row-wise aggregation by joining the CDI table with the Population table and applying dynamic filters for each row in the CDI table.
- The SQL query utilizes JOIN and filter conditions to aggregate population data according to the criteria specified in each row of the CDI table.
- Executing the query in DuckDB allows you to efficiently aggregate population data based on different filters for each row in the CDI table, providing valuable insights for data analysis.
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