Introduction to Databases in Data Science

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Introduction to Databases in Data Science

Data science involves extracting value and insights from large amounts of data to drive business decisions. It also involves building predictive models using historical data. Databases facilitate efficient storage, management, retrieval, and analysis of such large amounts of data.

Therefore, as a data scientist, you should know the basics of databases. Because they enable the storage and management of large, complex data sets, enabling efficient data exploration, modeling, and gaining insights. Let’s explore this in more detail in this article.

We'll start by discussing basic database skills for data science , including SQL for data retrieval, database design, optimization, and more. Then, we'll cover the main database types, their benefits, and use cases.

Essential Database Skills for Data Science

Database skills are critical for data scientists as they provide the foundation for effective data management, analysis, and interpretation.

Here is a breakdown of the key database skills a data scientist should know:

Image source: author

Although we try to categorize database concepts and skills into different buckets, they are consistent. When working on a project, you often need to understand or learn them along the way.

Now let’s review each of the above.

1. Database types and concepts

As a data scientist, you should have a good understanding of different types of databases such as relational and NoSQL databases and their respective use cases.

2.SQL (Structured Query Language) for data retrieval

SQL proficiency achieved through practice is a must for any role in the data space. You should be able to write and optimize SQL queries to retrieve, filter, aggregate, and join data from the database.

It's also helpful to understand query execution plans and be able to identify and resolve performance bottlenecks.

3. Data modeling and database design

In addition to querying database tables, you should understand the basics of data modeling and database design, including entity-relationship (ER) diagrams, schema design, and data validation constraints.

You should also be able to design database schema that supports efficient querying and data storage for analysis.

4. Data cleaning and transformation

As a data scientist, you must preprocess raw data and convert it into a format suitable for analysis. Databases can support data cleaning, transformation, and integration tasks.

Therefore, you should know how to extract data from various sources, convert it into a suitable format, and load it into a database for analysis. It is important to be familiar with ETL tools, scripting languages ​​(Python, R) and data transformation techniques.

5. Database optimization

You should understand techniques for optimizing database performance, such as creating indexes, denormalization, and using caching mechanisms.

To optimize database performance, use indexes to speed up data retrieval. Proper indexes improve query response times by allowing the database engine to find the required data quickly.

6. Data integrity and quality checks

Maintain data integrity by defining constraints for data entry rules. Constraints such as unique constraints, non-null constraints, and check constraints ensure the accuracy and reliability of data.

Transactions are used to ensure data consistency and ensure that multiple operations are treated as a single atomic unit.

7. Integration with tools and languages

The database can be integrated with popular analysis and visualization tools, allowing data scientists to efficiently analyze and present their findings. Therefore, you should know how to connect to and interact with databases using programming languages ​​such as Python and perform data analysis.

Familiarity with tools such as Python's pandas, R and visualization libraries is also necessary.

Summary: Understanding various database types, SQL, data modeling, ETL processes, performance optimization, data integrity, and integration with programming languages ​​are key components of a data scientist's skill set.

In the remainder of this introductory guide, we will focus on basic database concepts and types .

Image source: author

relational database basics

A relational database is a database management system (DBMS) that uses tables containing rows and columns to organize and store data in a structured manner. Popular RDBMS include PostgreSQL, MySQL, Microsoft SQL Server, and Oracle.

Let's dive into some key relational database concepts with examples.

relational database table

In a relational database, each table represents a specific entity , and relationships between tables are established using keys .

To understand how data is organized in relational database tables, it's helpful to start with entities and attributes .

You often need to store data about objects: students, customers, orders, products, etc. These objects are entities and they have properties.

Let's take a simple entity as an example, which is a "student" object with three properties: first name, last name, and grade. When storing data, entities become database tables and attributes become column names or fields. Each row is an instance of the entity.

Image source: author

Tables in relational databases consist of rows and columns:

• These rows are also called records or tuples , and
• These columns are called attributes or fields .

Here is an example of a simple "Students" table:

Student card	name	Last name	grade
1	Jane	Smith	A+
2	Emily	brown	one
3	Jack	williams	B+

In this example, each row represents a student and each column represents a piece of information about that student.

Understand the key

Keys are used to uniquely identify rows in a table. Two important key types include:

• Primary Key: The primary key uniquely identifies each row in the table. It ensures data integrity and provides a way to reference specific records. In the "Student" table, "Student ID" can be the primary key.
• Foreign Keys: Foreign keys establish relationships between tables. It references the primary key of another table and is used to link related data. For example, if we have another table called "Courses", the "Student ID" column in the "Courses" table might be a foreign key that references the "Student ID" in the "Students" table.

relation

Relational databases allow you to establish relationships between tables. The following are the most important and most frequently occurring relationships:

• One-to-one relationship: In a one-to-one relationship , each record in a table is related to one (and only one) record in another table in the database. For example, a "Student Details" table that contains additional information about each student might have a one-to-one relationship with the "Students" table.
• One-to-many relationship : One record in the first table is related to multiple records in the second table. For example, the Courses table can have a one-to-many relationship with the Students table, where each course is associated with multiple students.
• Many-to-many relationship : Multiple records in two tables are related to each other. To represent this, an intermediate table is used, often called a join table or linked table. For example, the Student Courses table can establish a many-to-many relationship between students and courses.

normalization

Normalization (often discussed under database optimization techniques) is the process of organizing data in a way that minimizes data redundancy and improves data integrity. It involves breaking up a large table into smaller related tables. Each table should represent a single entity or concept to avoid duplication of data.

For example, if we consider the "Students" table and the hypothetical "Addresses" table, normalization might involve creating a separate "Addresses" table with its own primary key and linking it to the "Students" table using foreign keys.

Advantages and limitations of relational databases

Here are some advantages of relational databases:

• Relational databases provide a structured and organized way to store data, making it easy to define relationships between different types of data.
• They support the ACID properties of transactions (atomicity, consistency, isolation, durability) to ensure data consistency.

On the other hand, they have the following limitations:

• Relational databases have challenges with horizontal scalability, which makes handling large amounts of data and high traffic loads challenging.
• They also require a strict architecture, which makes it challenging to accommodate changes in data structures without modifying the schema.
• Relational databases are designed for structured data with well-defined relationships. They may be less suitable for storing unstructured or semi-structured data such as documents, images, and multimedia content.

Explore NoSQL databases

NoSQL databases do not store data in tables in the familiar row-column format (hence are non-relational databases). The term "NoSQL" stands for "Not Just SQL" and indicates that these databases are different from the traditional relational database model.

The main advantages of NoSQL databases are their scalability and flexibility . These databases are designed to handle large amounts of unstructured or semi-structured data and provide more flexible and scalable solutions than traditional relational databases.

NoSQL databases include a variety of database types that differ in data models, storage mechanisms, and query languages. Some common categories of NoSQL databases include:

• key value storage
• document database
• column series database
• Graph database.

Now, let’s review each NoSQL database category, exploring their characteristics, use cases, and examples, advantages, and limitations.

key value storage

Key-value stores store data as simple key and value pairs . They are optimized for high-speed read and write operations. They are suitable for applications such as caching, session management, and real-time analytics.

However, these databases have limited query capabilities beyond key-based retrieval. So they are not suitable for complex relationships.

Amazon DynamoDB and Redis are commonly used key-value stores.

document database

Document databases store data in document formats such as JSON and BSON. Each document can have a different structure, allowing for nested and complex data. Its flexible schema allows easy processing of semi-structured data, supporting evolving data models and hierarchical relationships.

They are particularly suitable for content management, e-commerce platforms, directories, user profiles and applications where data structures are constantly changing. Document databases may not be efficient for complex joins or complex queries involving multiple documents.

MongoDB and Couchbase are popular document databases.

Column series storage (wide column storage)

Column family storage , also known as columnar database or column-oriented database, is a NoSQL database that organizes and stores data in a column-oriented manner instead of the traditional row-oriented manner of relational databases.

Column series storage is suitable for analytical workloads that involve running complex queries against large data sets. Aggregations, filters, and data transformations are generally performed more efficiently in column series databases. They help manage large amounts of semi-structured or sparse data.

Apache Cassandra, ScyllaDB and HBase are some column-family stores.

graph database

Graph databases model data and relationships in nodes and edges respectively. to represent complex relationships. These databases support efficient processing of complex relationships and powerful graph query languages.

As you can guess, these databases are suitable for social networks, recommendation engines, knowledge graphs, and generally data with complex relationships.

Examples of popular graph databases are Neo4j and Amazon Neptune.

There are many NoSQL database types. So how do we decide which one to use? well. The answer is: It depends.

Each category of NoSQL databases offers unique features and benefits that make them suitable for specific use cases. It's important to choose the right NoSQL database by considering access patterns, scalability requirements, and performance considerations.

To summarize: NoSQL databases offer advantages in flexibility, scalability, and performance, making them suitable for a wide range of applications, including big data, real-time analytics, and dynamic web applications. However, they come with trade-offs in data consistency.

Advantages and limitations of NoSQL databases

Here are some advantages of NoSQL databases:

• NoSQL databases are designed for horizontal scalability, allowing them to handle large amounts of data and traffic.
• These databases allow for flexible and dynamic architectures. They have flexible data models to adapt to various data types and structures, making them ideal for unstructured or semi-structured data.
• Many NoSQL databases are designed to run in distributed and fault-tolerant environments, providing high availability even in the event of hardware failure or network outage.
• They can handle unstructured or semi-structured data, making them suitable for applications that handle different data types.

Some restrictions include:

• NoSQL databases prioritize scalability and performance over strict ACID compliance. This may lead to eventual consistency and may not be suitable for applications requiring strong data consistency.
• Since NoSQL databases have different APIs and data models, the lack of standardization can make it challenging to switch between databases or integrate them seamlessly.

It’s important to note that NoSQL databases are not a one-size-fits-all solution. The choice between NoSQL and relational databases depends on the specific needs of the application, including data volume, query patterns, and scalability requirements.

Relational databases vs. NoSQL databases

Let's summarize the differences discussed so far:

feature	relational database	NoSQL database
data model	Table structure (table)	Diverse data models (documents, key-value pairs, graphs, columns, etc.)
data consistency	Strong consistency	eventual consistency
Schema	Well-defined architecture	Flexible or structureless
data relationship	Support complex relationships	Varies by type (limited or explicit relationship)
query language	SQL-based queries	Specific query language or API
flexibility	Not as flexible for unstructured data	Works with many data types including
Use Cases	Well-structured data, complex transactions	Large-scale, high-throughput, real-time applications

A note about time series databases

As a data scientist, you will also work with time series data. Time series databases are also non-relational databases but have more specific use cases.

They need to support storing, managing, and querying time-stamped data points (data points recorded over time), such as sensor readings and stock prices. They provide specialized functionality for storing, querying, and analyzing time-based data patterns.

Some examples of time series databases include InfluxDB, QuestDB, and TimescaleDB.

in conclusion

In this guide, we introduced relational databases and NoSQL databases. It's also worth noting that you can explore many more databases besides the popular relational and NoSQL types. NewSQL databases such as CockroachDB provide the traditional advantages of SQL databases while providing the scalability and performance of NoSQL databases.

You can also use an in-memory database, which stores and manages data primarily in your computer's main memory (RAM), rather than a traditional database that stores data on disk. This approach provides significant performance benefits because faster read and write operations can be performed in memory compared to disk storage.

Original link: Introduction to databases in data science (mvrlink.com)