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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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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.

tableau features
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 and prioritize
  • grouping and aggregating to summarize
  • creating on-the-fly calculations to express numbers in useful ways. A visual analysis application exposes these exploratory operations to ordinary people through easy-to-use interfaces.
Related: Tableau 9 Advanced Training

2). Data Visualization

Visual analysis means presenting information in ways that support visual thinking. Data is displayed using the best practices of information visualization. The right presentation makes it easy to organize and understand the information.

For example, critical information may be quickly found, and features, trends, and outliers may be easily recognized. One powerful way to evaluate any analysis tool is to test its effectiveness in answering specific questions.
At the most fundamental level, does the tool have the analytical power needed to answer the question?
At another level, how long does it take to answer the question? A successful visual analysis application unites data exploration and data visualization in an easy-to-use application that anyone can use.

Daily use Tableau commands

addusers (to group)
creategroup
createproject
createsite
createsiteusers
createusers
delete workbook-name or datasource-name
deletegroup
deleteproject
deletesite
deletesiteusers
deleteusers
editdomain
editsite
export
get url
listdomains
listsites
login
logout
publish
refreshextracts
removeusers
runschedule
set
syncgroup
version

YouTube tutorial for beginners:


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