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How to Build CI/CD Pipeline: GitHub to AWS

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 Creating a CI/CD pipeline to deploy a project from GitHub to AWS can be done using various AWS services like AWS CodePipeline, AWS CodeBuild, and optionally AWS CodeDeploy or Amazon ECS for application deployment. Below is a high-level guide on how to set up a basic GitHub to AWS pipeline: Prerequisites AWS Account : Ensure access to the AWS account with the necessary permissions. GitHub Repository : Have your application code hosted on GitHub. IAM Roles : Create necessary IAM roles with permissions to interact with AWS services (e.g., CodePipeline, CodeBuild, S3, ECS, etc.). AWS CLI : Install and configure the AWS CLI for easier management of services. Step 1: Create an S3 Bucket for Artifacts AWS CodePipeline requires an S3 bucket to store artifacts (builds, deployments, etc.). Go to the S3 service in the AWS Management Console. Create a new bucket, ensuring it has a unique name. Note the bucket name for later use. Step 2: Set Up AWS CodeBuild CodeBuild will handle the build proces
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Excel: 10 Key Topics You Need to Learn

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The below-listed topics help you get a solid footing in Excel Analytics. Just practice these 10 topics step by step and by completing all, you will be an expert in Excel. 10 Top Excel Topics Tables in Excel  Grabbing data from external sources  Cleaning data with functions  Working with Pivot tables  Writing Formulae for Pivot tables  Pivot Charts  How to use database functions  How to use statistics  Inferential Statistics  Descriptive statistics Also Read : 5 Tips why macros need in Excel
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How Hadoop is Better for Legacy data

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Here is an interview question on legacy data. You all know that a lot of data is available on legacy systems. You can use Hadoop to process the data for useful insights. 1. How should we be thinking about migrating data from legacy systems? Treat legacy data as you would any other complex data type.  HDFS acts as an active archive, enabling you to cost-effectively store data in any form for as long as you like and access it when you wish to explore the data. And with the latest generation of data wrangling and ETL tools, you can transform, enrich, and blend that legacy data with other, newer data types to gain a unique perspective on what’s happening across your business. 2. What are your thoughts on getting combined insights from the existing data warehouse and Hadoop? Typically one of the starter use cases for moving relational data off a warehouse and into Hadoop is active archiving.  This is the opportunity to take data that might have otherwise gone to the archive and keep it av
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Old School Guide Data Analyst Responsibilities

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The results of your analysis may be super meaningful and obvious to you, but they won’t be to anyone else. That’s because you know what questions you were looking to answer when you set out to do the analysis in the first place. Your Role-You know exactly what data the dataset includes and excludes. Plus you wrote the queries that ultimately produced the visualization or report you’re looking at. That’s a lot of contexts that you need to share in order for other people to understand what the numbers mean. Sharing Results-When sharing the results of your analysis, write out the conclusions you are drawing from the data and what business actions you think should be taken as a result of the analysis (e.g. our conversion decreased with this latest release and we should rollback). Not only do other folks perhaps not have the context to interpret the data correctly, they probably don’t find it as fascinating as you do and may not have the time to derive meaning from the data. Communi
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2 Top Tableau Unique Features

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Tableau is one of the most popular tools in data analysis. Learning the Tableau gives you so many options in data analysis career. You can download Tableau Software free version here . Get a complete understanding document on how Tableau works here . Read this post for advancing in your Tableau Career. Unique functionality in Tableau Tableau Software was founded on the idea that analysis and visualization should not be isolated activities but must be synergistically integrated into a visual analysis process. Visual analysis means specifically: 1). Data Exploration Visual analysis is designed to support analytical reasoning. The goal of the visual analysis is to answer important questions using data and facts. In order to support analysis, it is not enough to only access and report on the data. Analysis requires computational support throughout the process. Typical steps in the analysis include such operations as filtering to focus on items of interest sorting to rank
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Here are 5 Skills You need to Become SAS Data Analyst

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Want to know what will happen in the future? Find the most lucrative opportunities? Get insights into impending outcomes? No problem. With our SAS data mining software, you can: SAS Data Analyst. Simplify data preparation. Interact with your data quickly and intuitively using dynamic charts and graphs to understand key relationships. Quickly and easily create better models. Take the guesswork out of building models that are both stable and accurate using proven techniques and a drag-and-drop interface that's both easy-to-use and powerful. Put your best models into service. Fast. Spend less time and effort scoring new data using automated, interactive processes that work in both batch and real-time environments. The requirement varies from company to company. I am giving here the basic skills you need for a SAS data analyst Experience in SAS or R analytics Scripting languages of Python/JavaScript/VB Script SQL and PL/SQL Databases knowledge in Oracle, DB2, SQL Server Hadoop and Big
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