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Dev
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How to deploy an Apache web app using Oracle Cloud
- Create a VCN for the Apache web app using Oracle Cloud Infrastructure (OCI)'s virtual cloud network and VM compute instance.
- Create a public VM instance and SSH into it to install Apache web server.
- Allow public access to the VM web app by configuring security rules.
- Validate the web app by accessing it through the VM's IP address.
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Amazon
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118
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Embed textual data in Amazon RDS for SQL Server using Amazon Bedrock
- This post explores how to generate vector embeddings on Wikipedia data stored in a SQL Server database hosted on Amazon Relational Database Service (Amazon RDS) using Amazon Bedrock.
- Before we explore vector embeddings, let's discuss two key Amazon Web Services (AWS) services in this solution: Amazon RDS for SQL Server and Amazon Bedrock.
- Amazon RDS for SQL Server is a fully managed database service that simplifies the setup, operation, and scaling of SQL Server databases in the cloud.
- Amazon Bedrock is a fully managed service that offers a choice of industry leading foundation models (FMs) along with a broad set of capabilities that you need to build generative AI applications.
- The first step is to establish a connection between the RDS for SQL Server instance and Amazon Bedrock.
- Tokenizing and vectorizing content data into vector embeddings can be accomplished through various approaches.
- Prepare the data for insertion and insert the data into the new table by forming an INSERT statement.
- To test the vector similarity search, create a prompt to input a search string to search for the keyword, Warrior.
- Running this solution created a few AWS resources including an RDS for SQL Server database instance and an Amazon SageMaker Notebook instance. If you don't need these resources going forward, delete them to avoid unnecessary charges.
- This post has provided a comprehensive overview of how to generate vector embeddings, from setting up the environment and generating embeddings to exploring their applications and advanced techniques.
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Dev
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196
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SQL "SELECT INTO" vs "INSERT INTO SELECT" statements.
- The "SELECT INTO" statement copies data from one table into a new table. The "INSERT INTO SELECT" statement copies data from one table and inserts it into another table.
- The "SELECT INTO" statement creates a new table, while the "INSERT INTO SELECT" statement requires an existing table.
- The "SELECT INTO" statement can copy all columns or only selected columns, while the "INSERT INTO SELECT" statement can also copy all columns or selected columns.
- The "SELECT INTO" statement is commonly used for creating backup or temporary tables, while the "INSERT INTO SELECT" statement is used to transfer data between existing tables.
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Dev
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Top 🐘👀 Postgres Monitoring Tools 🧰 and Best Practices in 2024 🔝
- Postgres monitoring is crucial in 2024.
- Open-source tools like pg_stat_statements, explain.dalibo.com, and Prometheus Postgres Exporter offer monitoring solutions.
- Proprietary solutions like pganalyze and DataDog provide advanced performance monitoring for Postgres.
- Best practices include monitoring transaction ID wraparound, monitoring locks, avoiding blocking operations, and using a cloud Postgres service provider.
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Hitconsultant
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338
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Oracle Health Launches AI-Powered EHR, Clinical AI Agent, Cloud Apps
- Oracle Health is previewing its next-generation electronic health record (EHR) at the Oracle Health Summit, leveraging AI and cloud computing.
- The EHR is built on Oracle Cloud Infrastructure (OCI), offering enhanced security and a seamless user experience.
- Oracle Health also announced the Clinical AI Agent, Oracle Health Patient Portal, and Oracle Health Patient Administration cloud apps.
- The AI-powered EHR automates processes, delivers insights at the point of care, and simplifies tasks for physicians and staff.
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Medium
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205
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Populating a Vertica Database with Random Data
- To populate a Vertica database with random data, you need to define the structure of the sales table.
- The SQL statement to create the table captures essential attributes of sales transactions.
- Using SQL functions, you can insert 10 million random records into the sales table.
- Populating a Vertica database with random data can enhance testing, development, and data analysis efforts.
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Soais
1M
343
Optimizing Business Operations with UiPath RPA and Process Mining
- Process mining is a transformative strategy that, when integrated with RPA, offers organizations a pathway to significant operational enhancements.
- UiPath's process mining features include comprehensive data integration, intuitive dashboards, and insights for action.
- Real-world applications of process mining include enhancing the customer journey and streamlining human resources.
- Key benefits of process mining with UiPath include automated process discovery, data-driven insights, improved efficiency, compliance assurance, and intelligent automation.
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Hackernoon
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219
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How to Use Docker to Template Local Database
- Resetting a large local development database can be time-consuming.
- PostgreSQL has a built-in template system to speed up database replication.
- Copying data between volumes is possible when using Docker to avoid restoring the database dump.
- The author used the BusyBox Docker image to make the copying process faster.
- The author created scripts to automate the process of creating and switching between volumes.
- The first script is used to create a template database and accepts two required parameters: project name and database dump path.
- The second script is used to switch to the created template.
- Creating a tooling around your daily tasks can save you a lot of time.
- This approach saves the author a lot of time on a daily basis.
- The repository with the scripts is available on GitHub.
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Amazon
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389
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How Claroty Improved Database Performance and Scaled the Claroty xDome Platform using Amazon Aurora Optimized Reads
- Claroty, a cybersecurity solutions provider, has improved database performance and scaled the Claroty xDome platform using Amazon Aurora Optimized Reads.
- Aurora is a highly scalable and high-performance relational database engine that is fully compatible with MySQL and PostgreSQL.
- To address the challenges of its large volumes of data and complex queries, Claroty adopted the Aurora Optimized Reads to significantly reduce the latency associated with I/O operations and temporary storage operations.
- This enabled Claroty to manage the working dataset beyond the memory limitations of even the largest database instances, ensuring that Claroy gained better latency and throughput for queries that sort, join, or merge large volumes of data.
- API requests, which were previously delayed, were now processed more quickly, resulting in significant improvement in query performance.
- Aurora I/O Optimized led to substantial cost savings of 50% reduction in expenses, thereby strengthening Claroty’s ability to provide robust and reliable cybersecurity solutions.
- The adoption of these advanced database features enabled Claroty to overcome performance bottlenecks and reduce operational costs.
- Claroty’s platform provides the deepest asset visibility and broadest, built-for-CPS solution set in the market, comprising exposure management, network protection, secure access, and threat detection.
- The Claroty Platform enables organizations to effectively reduce CPS risk, with the fastest time-to-value and lower total cost of ownership.
- Itay Kirshenbaun is the Chief Architect at Claroty, and Pini Dibask is a Senior Database Solutions Architect at AWS.
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Medium
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453
MariaDB + phpMyAdmin Migration Disaster: “Not Enough Privilege to View Users”
- MariaDB + phpMyAdmin Migration Disaster: “Not Enough Privilege to View Users”
- In the world of web development, building dynamic and interactive websites requires a robust backend infrastructure.
- Programming languages like PHP, Python, and JavaScript play key roles in web development.
- MySQL is a powerful open-source relational database management system used for storing and retrieving data efficiently.
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Cloudblog
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96
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Unity Ads uses Memorystore to power up to 10 million operations per second
- Unity Ads, a mobile advertising platform migrated their workloads to Memorystore for Redis Cluster, a fully managed service designed for high-performance workloads.
- Unity infrastructure now handles up to 10 million Redis operations per second for a single instance.
- They gained a more reliable and scalable infrastructure, reduced costs, and gained time to focus on high-value activities.
- Memorystore for Redis Cluster service offers significantly high throughput and data capacity while maintaining microsecond latencies, consolidating larger workloads into a single, high-performance cluster.
- Scalable feature of Memorystore for Redis Cluster is that it can scale with zero downtime, and users can expand their clusters to handle terabytes of keyspace with a simple click or command.
- Memorystore offers automatic replica node management, placing them in different zones from their primaries to protect against outages and thus enhance reliability.
- Unity Ads can now scale efficiently in production, allowing them to adapt to changing demands without disrupting their services.
- With Memorystore, there is no need to worry about erratic behavior, which was present in their previous DIY Redis cluster setup.
- Unity Ads has gained operational improvements by moving to Memorystore, including cost efficiencies and simplicity to enable persistence on it.
- Memorystore has opened up new possibilities for how Unity Ads can architect their systems, allowing them to confidently expand their use of Redis across more of their infrastructure to lower costs and improve performance.
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Dbi-Services
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248
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Mastering SSIS with Excel – Resolving the ‘Microsoft.ACE.OLEDB.16.0 Provider is Not Registered’ Error
- Encountered the 'Microsoft.ACE.OLEDB.16.0 Provider is Not Registered' error while working on a data extraction task using SSIS.
- Provides a guide on migrating SSIS packages from SQL Server 2017 to SQL Server 2022 using Visual Studio 2022.
- Shares steps on deploying SSIS packages to the SSIS Catalog.
- Offers solutions to resolve the 'Microsoft.ACE.OLEDB.16.0 Provider is Not Registered' error.
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Dev
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192
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These 10+ comparisons cover entire SQL concepts, Is it?
- The article highlights some important SQL concepts and key terminologies that are frequently misunderstood or confusing.
- 10+ comparisons are made in a tabular form in the article to clarify the differences between these concepts.
- The primary key uniquely identifies every record in a table and can not contain null values, while a foreign key is used to link two tables together.
- Clustered Index stores data rows in the table based on key values, while non-clustered index stores a separate structure from data rows with pointers back to the data rows.
- Inner join returns matching value rows in both tables, while outer join returns all records when there is a match in either left, right or both tables.
- Where filters records before any groupings are made in the result set,
- Having filters records after the group by clause is applied.
- Union combines the result sets of two queries and removes duplicate records, whereas union all combines the result sets of two queries but doesn't remove duplicates.
- Delete removes rows one at a time and logs each row deletion, while truncate removes all rows from a table without logging individual row deletions.
- Drop removes a table entirely, while delete removes rows based on the condition but keeps table structure intact.
- Char is a fixed-length data type that always occupies fixed storage space, while varchar is a variable-length data type that occupies space based on the actual length of the data.
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Mysql
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MySQL: information_schema.tables performance regression
- Queries against i_s.tables should be fast and scale to a million tables. In MySQL 8.x, queries against data dictionary tables do not scale well in case of a large number of tables in a single schema e.g. 313k tables. MySQL 8.x uses hidden InnoDB tables to store the data dictionary. Since queries against the data dictionary are run very often and often implicit, it is a matter of great concern when the system behaves so poorly in such cases.
- Queries against the DD do not scale well, in my case against 313k tables in a single database. Even explicit queries against information_schema tables or SHOW TABLES LIKE statements are slow and a drop database on a schema with 313k tables would never terminate.
- This issue arises due to the heavy usage of these queries for almost everything, including internally and implicitly. This slow down is even worse when the number of tables is large in a single schema, rendering it a matter of concern.
- Asking for a table in the million schema is slow even when asking for tables from other schemas with only 6 tables is slow. Even command show tables like is affected by the slowdown.
- Queries against i_s.tables should be fast and scale to a million tables. But, not only are explicit queries against I_S.TABLES and SHOW TABLES LIKE statements slower, it's also a matter of great concern when it fails in cases such as
- MySQL 8.0 uses hidden InnoDB tables to store the data dictionary, and queries are not scaled well in cases with a large number of tables in a single schema; this causes concern as queries against DD are run often and often implicit.
- Drop a database on a schema with 313k tables would never terminate. Several queries against I_S.TABLES are simpler than the query plan shows and experience worse performance than anticipated.
- This issue causes a severe slowdown since these queries are run often and often implicit, and not just when they're physically sent to the server.e.g. events processing, and distributed transactions.
- Even queries with table_schema from a schema with only 6 tables are slow. Queries against i_s.tables should scale well to millions of tables, but MySQL 8.x has hidden InnoDB tables to store a data dictionary and causes slow downs.
- Queries against the data dictionary tables in MySQL7 were mostly CPU-bound and started to be more IO-bound from MySQL 8. As a result, heavy-disk activity with long latencies caused performance regression in MySQL8 when more than a few thousand tables exist in a schema.
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Mysql
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MySQL: dual passwords
- MySQL allows multiple passwords on a single account for regulatory requirement.
- There is no native view or reporting query that can give an overview of all accounts and their password usage.
- Log analysis with elevated log levels is required to track password usage.
- Rotating machine accounts and using mechanisms like vault for login information distribution is recommended.
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