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Hackernoon

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Understanding Measures of Centrality: A Deep Dive into Mean, Median, and Mode

Exploratory Data Analysis (EDA) is crucial in understanding data distribution and deciding next steps.
Mean (average) calculates central value by summing all values and dividing by the count.
Median is the middle value in sorted data and is not affected by outliers.
Mode is the most frequent value and is useful for categorical data.

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Amazon

407

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Migrate Oracle applications and databases using AWS Application Migration Service

Migrating an Oracle application and its underlying database to the cloud can be inherently complex.
AWS Application Migration Service accelerates the migration of applications to Amazon Web Services (AWS) by automatically replicating entire servers at the block level.
In this post, we show you the process of migrating Oracle E-Business Suite to AWS using Application Migration Service.
The high-level steps to implement this solution are as follows: create a VPC endpoint for Application Migration Service, create an AWS Identity and Access Management (IAM) user and roles, generate temporary credentials for Application Migration Service, install the AWS Replication Agent in the source server, create and configure an Amazon EC2 launch template, activate post launch settings for AWS Systems Manager Agent (SSM Agent) installation, check data replication status in the AWS Management Console for Application Migration Service, launch an Application Migration Service test instance for Oracle E-Business Suite database and application servers, configure the Oracle E-Business Suite database tier on the target database server test instance, configure the Oracle E-Business Suite application tier on the target application server test instance, mark as ready for cutover in the Application Migration Service console, launch cutover Instances in the Application Migration Service console, configure the Oracle E-Business Suites database and application tier on the cutover instances, and finalize cutover in the Application Migration Service console.
AWS Application Migration Service uses EC2 launch templates to launch, test, and cutover EC2 instances for each source server.
After replication is complete, the server is launched in AWS, maintaining the original server’s configuration, allowing for a seamless transition to the cloud.
The AWS Replication Agent will be uninstalled from the source server within 10 minutes.
This post helps you to successfully migrate the environment, including the critical Oracle ASM disk group, from on premises to the AWS Cloud.
Using the Application Migration Service capabilities, you can achieve a seamless migration of both application and database tiers while effectively addressing the challenges of migrating Oracle ASM.
The process outlined in this post helps you to successfully migrate the environment, including the critical Oracle ASM disk group, from on premises to the AWS Cloud.

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Mysql

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Using Dolphie for MySQL focus monitoring & performance tuning.

Dolphie is a python tool developed by Charles Thompson to monitor and fine-tune MySQL instances
It can be started as a standalone tool that feeds off of performance_schema’s goodness. Or it can be configured to record and replay statistics for interactive investigation into past events
There isn’t another MySQL instance as an auxiliary database repository nor do users have to install an agent across all nodes on the platform
The tasks to install are simple: install dolphie, provide the instances and configuration for the user's access and hit enter
Focus on key strokes after Dolphie is up and running. Clickable TUI elements can show meaningful CPU, memory, table cache, threads, disk I/O, and other data
Dolphie snitches if a user changes variable settings like innodb_lru_scan_depth from 1024 to 256
Dolphie also lets users play with parameters and manage to tune settings. Dowside is that it turns off implicitly before the resize, and then enabled afterwards
Dolphie can show error log contents in a searchable and orderable new screen
Dolphie can give users a memory overview for all users, hosts, and code areas: current & total. Code area useful to see if temptable is being abused
Dolphie provides features like “pon da replay” which helps record and replay with Dolphie

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Dbi-Services

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DPR and Dbvisit Standby on ODA

Oracle Database Appliance X7, X8 and X9 will require the use of Data Preserving Reprovisioning (DPR) to apply the patch coming from 19.20 and previous releases.
Unlike traditional patching, DPR erases the system disks but keeps data on ASM/ACFS disks.
Dbvisit Standby is a smart solution for those needing a Disaster Recovery solution without the need for buying Enterprise Edition.
Best practice to prevent the erasing of configuration files when using DPR is to set up Dbvisit Standby in a dedicated ACFS filesystem.
DPR will require a longer downtime of your ODA, several hours at least, meaning that you will need to switchover your databases to another ODA with Data Guard or Dbvisit Standby.
Classic patching does not require any additional operation when using Dbvisit Standby compared to Data Guard.
The first failure in Dbvisit Standby when using DPR is easy to fix, just remove the service associated with the database before DPR is activated.
If metadata in OCR is inconsistent while using DPR, it should be related to metadata in the ODA registry.
Using DPR on an ODA running Dbvisit Standby works fine, but it is important to check and fix the role of the database in the ODA registry if stuck at the preupgradereport.
DPR aims to make upgrading easier, but patching is not always a trivial task. Dbvisit is a powerful tool that can simplify the patching process.

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Dev

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How We Built 🔧🦾 the SQL Autocomplete Framework ⚙️🧱 with ANTLR4

Bytebase, a DevSecOps solution for databases, has built an autocomplete framework for its SQL Editor that supports different SQL dialects and offers extensibility and strong completion capabilities.
An ANTLR4-based parser processes user input and generates a parse tree or abstract syntax tree representing the syntactic structure of the input according to the grammar.
Autocomplete follows a syntax-agnostic, core-based approach that leverages an automaton created from syntax rules, allowing predictions that are syntax-agnostic and extensible.
The approach includes a core that identifies terminal symbols (keywords) and grammar rules of interest (identifiers), and post-processing that converts candidate rules to corresponding strings and return them to the user.
Bytebase SQL Editor supports common autocomplete scenarios relative to table names, column names, and function names.
The ANTLR4-based autocomplete core is implemented in Golang and used in five different SQL dialects: Oracle, SQL Server, PostgreSQL, MySQL, and DynamoDB.
The Bytebase SQL Editor is open-source, and developers can visit the Bytebase console to interact with the database from the SQL Editor and embed the SQL Editor into their internal web portal.
The ANTLR4 code that serves as the basis for the SQL Editor is also open-sourced, with Bytebase having identified areas of improvement in the ANTLR4 Golang runtime.
Future articles will look more closely at ANTLR4-based autocomplete core implementation, syntax-specific processing, and how to integrate it with LSP to provide a complete and robust SQL autocomplete.
Bytebase's tech stack choice is Golang for the ANTLR4-based autocomplete core, and it has ensured cross-SQL dialect support while reducing development and maintenance costs significantly, allowing a small team to maintain autocomplete functionalities for multiple dialects.

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Analyticsindiamag

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AI is the New BI

ThoughtSpot, a California-based BI and analytics company, has launched Spotter, an agentic AI tool that functions as a virtual analyst to assist businesses.
Spotter enables conversational interactions with data, allowing users to pose complex, multi-step questions in natural language and obtain accurate responses.
ThoughtSpot aims to redefine self-service BI by addressing the challenges of traditional self-service BI, and providing an analytics platform built ground up for AI.
ThoughtSpot's vision is to enable users to understand data through a conversation with their data, positioning itself as the 'Google plus ChatGPT for data.'

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Dev

444

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Database Normalization

Database normalization helps to organize data in a structured way by eliminating redundancy and ensuring data integrity within relational databases.
A relational database is a type of database that stores data in tables, which are structured into rows and columns consisting of table, rows, and columns.
Primary key and Foreign key are the two types of keys used in relational databases to link data from different tables.
Redundant data means storing the same data in multiple places, which wastes disk space and creates maintenance problems. Inconsistent dependency happens when data in a database relies on other data in a way that is unpredictable or incorrect.
Normalization has different forms, and each aims to eliminate specific types of redundancy or inconsistency to create a well-organized database. First Normal Form (1NF) eliminates repeating groups in individual tables by creating a separate table for each set of related data. Second Normal Form (2NF) removes partial dependency. Third Normal Form (3NF) removes transitive dependencies.
Elementary key Normal Form (EKNF) further refines 3NF to ensure all functional dependencies are straightforward and clear, without any complex dependencies. Boyce–Codd Normal Form (BCNF) eliminates functional dependencies except if A is a candidate key. Fourth Normal Form (4NF) deals with multivalued dependencies and Fifth Normal Form (5NF) is the highest level of normalization.
Denormalization is the process of adding redundant data to a database to improve performance by reducing processing across multiple tables.

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Amazon

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How Coinbase provides trustworthy financial experiences through real-time user clustering with Amazon Neptune

Coinbase migrated their user clustering system to Amazon Neptune Database to handle vast amounts of data related to transactions, user behavior, and market trends.
The original datastore for the clustering system was not graph-based, and clusters needed to be precomputed and stored in a NoSQL database.
Pre-calculating clusters became more challenging with increasing complexity over time and necessitated a high number of database updates to support each specific use case.
Graph databases are designed to manage complex, interconnected data structures, allowing representation and querying of relationships between entities.
Coinbase needed a solution that can handle frequent updates to both data and relationships, and graph database can perform real-time traversals of connections and relationships.
Neptune Database addresses several technical challenges faced by large-scale graph database implementations. Because it is fully managed, Coinbase can eliminate significant operational overhead while providing flexibility in data modeling and querying.
Coping with write-heavy workload characterized by frequent updates arising from multiple events, Coinbase micro batches into the same transaction achieving desired ingestion rates through 20 writes per second.
Coinbase achieved new use cases, performance efficiency, reliability, cost optimization, and visualizations with 30% savings in storage costs by eliminating redundant information.
Coinbase's journey with Neptune Database showcases the power of graph databases in solving complex, interconnected data challenges at scale.
Neptune Analytics is a memory-optimized graph database that helps you find insights faster by analyzing graph datasets with built-in algorithms.

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Amazon

376

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Using attribute-based access control for tag-based access authorization with Amazon DynamoDB

AWS announced the general availability of attribute-based access control (ABAC) for Amazon DynamoDB that provides authorization strategy depending on attributes to define permissions based on AWS Identity and Access Management (IAM) entities and resources called tags.
Using attribute-based access control (ABAC) with DynamoDB offers fewer policies, automatic permission management, alignment with a corporate directory.
The complexity of permission management within the policies increases when new IAM principals are added regularly, and policy management is no longer scalable.
ABAC is an authorization strategy that defines permissions based on attributes called tags. You can attach tags to (IAM) entities such as users and roles, and to AWS resources such as DynamoDB tables.
With new IAM principals being added to the policies regularly, the complexity of permission management within policies increases, and policy management is no longer scalable.
Benefits of using ABAC with DynamoDB include fewer policies, automatic permission management, alignment with a corporate directory, and monitoring actions that users have performed.
Auditing your policies is essential to avoid surprises from authorization changes with your applications that connect to DynamoDB after ABAC is enforced.
If your account is not enabled for DynamoDB ABAC, the tag-based conditions in your identity-based or other policies that are intended to act on DynamoDB tables or indexes are evaluated as if no tags are present for your tables.
Using ABAC with DynamoDB simplifies permission management, segmentation of access control for DynamoDB tables and indexes, and team access as the organization expands.
There is no additional cost to use ABAC with Amazon DynamoDB.

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Dev

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Create a Relational Database Application

A company is implementing a robust RDBMS to streamline its employment application process.
Features of the project include authentication system, dashboard, applicant CRUD, application CRUD, education history CRUD, employment history CRUD, reference CRUD, print functionality, and search.
Technologies used for the project include React, Node.js, Express, MySQL, bcrypt, and jsonwebtoken.
The project includes database design with metadata, normalization, and constructing an Entity-Relationship Diagram.

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Cloudblog

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New Cassandra to Spanner adapter simplifies Yahoo's migration journey

It is now easier to switch from Cassandra to Spanner with the introduction of Cassandra to Spanner Proxy Adapter, an open-source tool for plug-and-play migrations of Cassandra workloads to Spanner, without any changes to the application logic.
Spanner provides strong consistency, high availability, virtually unlimited scalability, and a familiar relational data model with support for SQL and ACID transactions for data integrity.
The new adapter simplifies migrating a Cassandra-backed application to point to Spanner with no migration needed for application code.
Migration from Cassandra to Spanner can be a transformative step for businesses seeking to modernize their data infrastructure, unlock new capabilities, and accelerate innovation.
Some of the latest Spanner capabilities include enhanced multi-model capabilities, improved performance for analytical queries with Spanner Data Boost, and unique enterprise features such as geo-partitioning and dual-region configurations.
The proxy adapter vastly simplifies migrating a Cassandra-backed application to point to Spanner.
The new proxy adapter presents itself as a Cassandra client to the application.
The proxy adapter supports OpenTelemetry to collect and export traces to Cloud Trace.
Advantages of Spanner over Cassandra includes: easier operations, multi-model capabilities, better performance, stronger consistency, and scalability, and familiar relational data model.
Download the new Cassandra to Spanner proxy adapter, and try it out on a Spanner Free Trial instance at no cost today.

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Siliconangle

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On theCUBE Pod: Business as code and the huge opportunities for tech giants

Code-based business automation, known as business as code, essentially is going to bring a DevOps-like methodology to business applications.
AI creates derivative benefits as friction is removed in work.
Business as code would represent a transformative approach to automating processes and would essentially automate all those unautomated processes that companies haven’t been able to automate.
Cloud giants such as AWS, Microsoft and Google are competing to power this next wave of technology with cost-efficient, scalable hardware and tools.
Google and Microsoft have a huge opportunity, as does Meta, as do these large companies like Dell, JPMorganChase, HPE, to essentially teach their customers how to go forward and build solutions around AI and automation.
SC24 and KubeCon + CloudNativeCon NA 2024 are important shows because they will set the agenda for the next year.
New generative AI stacks are emerging and it's just another layer of innovation; applications are getting smarter, bringing the reality cycle of 'Show me the money, show me the value.'
DevOps brought harmony between development, operations and infrastructure. Business as code will bring a similar harmony between technology and business productivity.
AI will start to do for productivity what serverless did for infrastructure.
Business as code concept is going to change the deployment of applications and give this agentic layer some programmability to it, like DevOps did.

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Medium

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Exploring MySQL Math Functions: A Quick Guide

MySQL math functions are frequently used in finance, data analysis, and scientific applications.
Some commonly used math functions include ABS(), COS(), ACOS(), DEGREES(), FLOOR(), POW(), and MOD().
These functions simplify complex calculations and are beneficial in data filtering and transformation tasks.
For more detailed guidance, refer to MySQL's official documentation or advanced SQL tutorials.

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Marktechpost

148

GraphAide: Building and Utilizing Knowledge Graphs for Domain-Specific Digital Assistants

Researchers from the Pacific Northwest National Laboratory have proposed GraphAide, an advanced LLM-based capability that offers insights into domain-specific data and allows users to ask natural language questions.
GraphAide introduces a comprehensive methodology and reference architecture to integrate GenAI with semantic web technologies through a modular and extensible RAG approach.
GraphAide's architecture combines agentic and chain-based approaches to create a complex RAG system that utilizes multiple LLM instances for diverse tasks.
GraphAide demonstrates its effectiveness through the generation of comprehensive KGs from news articles and shows improved NER and relation extraction quality compared to baseline approaches.

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Dev

139

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How to Fix "MySQL Shutdown Unexpectedly"

To fix the "MySQL shutdown unexpectedly" error, follow these steps:
Step 1: Backup your current data folder.
Step 2: Delete specific files from the data folder.
Step 3: Restore files from your backup.
Step 4: Restart MySQL.

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