Posts

Showing posts with the label data science data to insights

Featured Post

14 Top Data Pipeline Key Terms Explained

Image
 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...

How to Identify Data Relevant for Data Science Analytics

Your government, your web server, your business partners, even your body. While we arenā€™t drowning in a sea of data, weā€™re finding that almost everything can (or has) been instrumented. We frequently combine publishing industry data from Nielsen Book Scan with our own sales data, publicly available Amazon data, and even job data to see whatā€™s happening in the publishing industry. Data is everywhere Sites like Infochimps and Factual provide access to many large datasets, including climate data, MySpace activity streams, and game logs from sporting events. Factual enlists users to update and improve its datasets, which cover topics as diverse as endocrinologists to hiking trails. How the data is growing Much of the data we currently work with is the direct consequence of Web 2.0, and of Mooreā€™s Law applied to data. The Web has people spending more time online and leaving a trail of data wherever they go. Mobile applications leave an even richer data trail since many of them a...