In data, aggregation is the process of collecting data from various sources and then summarising or combining it to provide a holistic view. Think of it like making a scrapbook—you gather bits from here and there and organise them all in one place.
This is the process of gathering and importing data from different locations and storing it in a central place for easier analysis. Imagine gathering all your important files from different rooms in your house and placing them in one drawer to keep them together.
Data integration is all about merging data from different sources so that they all work together smoothly. Picture trying to assemble pieces of a puzzle from different boxes—the goal is to make them fit into a single picture!
Harmonisation means making sure data from different sources follows the same format and conventions, so they work together without hiccups. It’s like deciding that everyone in a group chat will use the same date format, so there’s no confusion.
ETL stands for Extract, Transform, Load—this process extracts data from a source, cleans or formats it, and loads it into a database. ELT is a newer version where you extract, load it as-is, and then transform it later. Think of ETL as sorting laundry before putting it away, and ELT as tossing it all in a drawer first, then organising it.
Cleaning is about correcting errors, removing duplicates, and ensuring consistency in your data, like making sure all dates are in the same format. Think of it like proofreading an essay to catch typos before you hand it in.
These are all ways to store large amounts of data:
Data Lake holds raw data, like a “junk drawer” for everything.
Data Warehouse holds clean, organised data, like a well-ordered filing cabinet.
Lake House combines elements of both—think of it as a filing cabinet in your junk drawer for more flexibility.
This term covers the ongoing upkeep of your data—storage, organisation, security, and accessibility. Like making sure your personal files are backed up, organised, and easy to find when needed.
Visualisation means showing data in a way that’s easy to understand, like a chart or a dashboard. Imagine turning a complicated report into a colourful infographic—it’s much simpler to interpret!
Machine learning is a type of AI where systems learn from data to make predictions or decisions. It’s like training a pet with treats to do a trick—the data is the treat, and the trick is the AI’s prediction!
This is the process of choosing and shaping the “features” (variables) that a machine learning model will use. It’s like deciding which ingredients to include in a recipe to get the best result.
This refers to expert guidance provided to help you make the most of your data, both now and in the future. Think of it as getting advice from a travel guide to make sure you’re on the best path for your journey.
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