Data on GCP

1. Data on GCP

Hello and welcome back! In this video, let’s delve deeper into how GCP helps businesses with their data.

2. Data-driven culture

Data has become an invaluable asset for organizations across all sectors. A data-driven culture is one that leverages data systematically to inform decisions, drawing insights that lead to improved strategies and operational efficiencies. By relying on data, businesses can transcend intuition-based approaches, making objective decisions that are supported by tangible evidence.

3. Data-driven culture

The usage of data begins with efficient storage. Cloud platforms like GCP offer vast storage capabilities, allowing for the collection, analysis, and application of data on a scale that was previously infeasible for many organizations. This is crucial for a data-driven culture as it enables the processing and analysis of large datasets in real-time, facilitating more dynamic and responsive business practices. This means organizations can pivot quickly, adapt to market changes, understand customer behavior in greater depth, and optimize their operations.

4. Decoding data types

Understanding the nature of the data is crucial in applying the correct tools and storage solutions. Structured data is the most recognizable type of data. This consists of tables and spreadsheets, it is relational in nature as a row-column relationship links each data point. It is robust but not very flexible due to its rigid schema. It is also difficult to scale up. GCP’s Cloud SQL is the service that houses structured data. Unstructured data consists of multimedia files and data objects. Non-relational in nature, this data type includes images, audio, and raw text files. Due to its lack of structure, it is the most flexible and scalable. Unstructured data is stored in file storage, like Google Cloud Storage.

5. Semi-structured data

There is also a category of data that is semi-structured. We have encountered one of these before, an organization tree. Trees are examples of non-relational data that are semi-structured. The "semi-structure" is a hierarchy in this case. Another type of semi-structured is key-value pairs. The English dictionary consists of key-value pairs. Each word is a "key" and each definition is a "value". We look up a key (word) in the dictionary to find a value (definition). One key could have a single or multiple values, making it hard to efficiently store key-value pairs in traditional row-column databases.

6. Semi-structured data

But this lack of rigid structure makes semi-structured data highly flexible, scalable, and fast. This makes it ideal for big data that does not follow a schema, like emails, social media posts, insurance claims, and health records, among others. GCP offers Bigtable which can handle both structured and semi-structured data.

7. GCP in retail

Imagine a retail company that wants to enhance its operational efficiency and customer experience using GCP services.

8. GCP in retail

For their transactional data, they might employ Cloud SQL. The company can manage inventory and sales data at a global scale using just one service. Their mobile app data includes customer profiles and social media posts. This is semi-structured data that is used to generate product recommendations for each customer. Therefore it also requires frequent and fast access. Semi-structured and quick access? This sounds like a job for Bigtable. The company's multimedia files, such as product images and promotional videos, represent unstructured data. This type of content is ideally stored in Cloud Storage.

9. Let's practice!

Now let’s review in the exercises what we have learnt about data on GCP!

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