A Beginner's Guide to Database, Data Warehouse, and Data Lake

??Data Management in Today's World

In today's digital age, where information is incredibly important, data management (DM) has become a crucial tool for organizations. It involves using different methods, techniques, and tools to keep data organized and easy to access, no matter what kind of data it is.

Companies use it to make sure they can find the data they need for different things like solving problems, creating apps, or running their business. With the growing importance of using data to make decisions and do cool stuff like predicting the future or teaching computers to learn, good data management is now super important for organizations that want to succeed in today's data-driven world.

Ways to Manage Data:

To manage data effectively, we use three special tools:

1. Databases
2. Data warehouses
3. Data lakesEach of these tools has its unique strengths, and we choose the one that fits our needs, like picking the right tool for the job.

??Exploring Database

Definition:

Database is simply a structured and systematic way of storing information to be accessed, analyzed, transformed, updated and moved (to other databases).

To begin understanding databases, consider an Excel notebook or Google sheet.?Spreadsheets?like these are a basic form of a table. Databases are almost exclusively organized in tables and those tables have rows and columns.

Purpose of Database:

• Databases are engineered to efficiently retrieve, update, and manage data.
• Designed to capture and record data via OLTP (Online Transactional Process)
• They excel at handling transactional data, which refers to data that changes frequently and is essential for day-to-day operations.
• Data is highly detailed.

??Real-Life Use Case: Library's Database

Imagine a library's database, where they keep track of books and borrowers. This database consists of tables with information about library members, including their names and contact details, and details about the books, such as titles, authors, and borrowing records. Just like an Excel spreadsheet, these tables have rows for each member or book and columns for specific details like names, titles, and due dates.

?Problems with Database:

Database for data management worked well for a long time because data volumes were small, and relational databases were simple and reliable.

But when the Internet came along and brought heaps of data with it, companies faced some big problems. They had so much data that using just one database wasn't enough. Databases struggled with the volume, affecting performance and data management. So, they started making lots of separate databases, each for different parts of their business to handle all this new data.

As the volume of data just continued to grow, companies often ended up with dozens of disconnected databases with different users and purposes, and many companies failed to turn their data into actionable insights. Companies needed a better way to manage and understand their data. This is where Data warehouses came into existence.

??Birth of Data Warehouses:

Ever wondered why it's called a data warehouse? Well, it's like a giant library for data, where you can store a massive amount of information neatly. Let's explore what a data warehouse is, and how it tackled the limitations of regular databases.

Definition:

A data warehouse is a big, organized storage system designed to collect and store data from all over a company. It's like a super-sized library where you can keep everything – data about customers, products, sales, and more. This data is stored in a way that makes it easy to find and analyze.

Purpose of Data Warehouse:

• Used for analytical processing or OLAP ( Online Analytical Processing )
• Created to analyze huge amounts of data
• Data is refreshed from source periodically using ETL process.
• Data stored is summarized implies faster processing for analytics.

?Solving Database Limitations:

Here's how data warehouses saved the day:

1. Centralized Storage: Data warehouses became the one place to gather data from various sources. It's like gathering all your books from different shelves into one library.
2. Organized Data: They arrange data in an organized way, making it easy to analyze and find valuable insights. It's like sorting your book collection by genre or author.
3. Time Travel: Data warehouses can also store historical data, like records of sales and customers over the years. This helps companies look back in time to understand trends.
4. Analytical Power: With all the data neatly organized, data warehouses make it easier to ask complex questions and find answers. They're like a super-smart librarian who can quickly find the right material for you in a massive library with books, journals, and papers scattered all over the place.

As data volumes grew even larger (big data), and as the need to manage unstructured and more complex data became more important,

?Data warehouses had limitations:

• Big IT projects using data warehouses can be expensive to keep up.
• Data warehouses are mainly for business reports and intelligence, not for other things like machine learning use cases.
• They can't handle different kinds of data very well and aren't very flexible.

So, people wanted something more flexible, like a big digital playground for data. This made people look for a different solution: data lakes, which are like big storage places for all kinds of data in different shapes and sizes.

??Birth of Data Lake:

Data Lake got their name because it's like a vast, open lake where you can throw everything you have.

Definition:

A data lake is like an enormous digital storage space, a bit like a super-huge computer file cabinet. But here's the cool part – it doesn't care what the data looks like. It can hold all sorts of stuff: structured, unstructured, messy, clean, and more. It's a place where you can keep all your data, whether it's numbers, words, pictures, or videos.

Purpose of Data Lake:

• Designed to capture any type of data ( video, image, text, csv, document, graph, json )
• Made for large amounts of data
• Raw data is used for ML and AI modelling where processed data can be used for Analytics and Reporting.
• Can be organized and put into Databases or Data warehouses

??Solving Data Warehouse Limitations:

Here's how data lakes changed the game:

1. All Data Welcome: In a data lake, you can toss in any kind of data. It's like having a huge drawer where you can put books, toys, clothes, and whatever you want, without worrying about making everything neat and tidy.
2. Flexibility: Data lakes are super flexible, making them great for exploring data. You don't need to decide how you're going to use the data when you put it in. You can figure that out later. It's like a big box of LEGO – you can build whatever you want.
3. Big Data Playground: Data lakes allow the use of diverse, unstructured data, making them ideal for training and powering machine learning models.

??When To Use What ?

• When looking at all three, they are all different and used for different purposes so no one option is better than another for your data.
• If you are using data just to record transactions then prefer database.
• If you have large amount of data that is just too much for your database to handle then you might need a data warehouse.
• If you have all this data and have no idea what to do with or it is unstructured, semi-structured data that you can’t fit into database then definitely go for data lake.

You can use only one or all theree within one company as per needs of data management.

As we wrap up our exploration of databases, data warehouses, and data lakes, we're left with a lingering curiosity about the emerging concept of the Data LakeHouse ???? and the cutting-edge technologies transforming industries.

Stay tuned for a journey into the future of data management!

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