Given its flat nature, a relational database is not suitable to represent hierarchical data. However, thanks to some tricks, you can transform a relational database into good storage for hierarchical data.
In this article, I cover the following topics:
Hierarchical Data is a data structure where items are related to each other in a parent/child relationship. The following figure shows an example of hierarchical data:
Image by Author
At the first level, there is a parent class, called Animal, which is the father (or root) of all the other classes. Then, at the second level, there are three children, Mammal, Bird, and Fish, all at the same level. Finally, there is a third level, with all the single species (e.g. Cat, Dog, and Lion for Mammal).
Looking at the previous figure, we can say that Mammal, Bird, and Fish are brothers, while Cat, Pheasant, and Shark are cousins.
Hierarchical Data is well represented by a tree, thus a graph database could be the best solution to represent it. Alternative solutions, such as NoSQL databases also could fit, as described in this Stackoverflow thread.
Anyway, a relational database also could be exploited to store hierarchical data. In the remainder of the article, I describe how to do it.
2 Converting Hierarchical Data into a Relational Table
To convert a hierarchical data structure into a relational table, different strategies could be adopted. In this article, I describe two methodologies:
In the Adjacency List Model each item stores a pointer to its parent. Practically, a new attribute, describing the level in the hierarchy, should be added to the classical data properties. Let us see an example, to understand what I mean.
I suppose to have the previous Animal structure, with three levels of hierarchy. I also suppose that for each animal, the following attributes are available:
Thus, I can build the following schema for the table Animals:
parent represents the previous level in the hierarchy. As a result, I could produce the following table:
Image by Author
The main advantage of the Adjacency List Model is its simplicity, while the main drawback involves deletion, which could be very dangerous since it could lead to orphan items.
In the nested set model, the tree is transformed into nested containers, like Continue Reading on Medium
SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:
1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.
2. Every database seller needs an approach to separate its item from others.
Right now, contrasts are noted where fitting.
#programming books #beginning sql pdf #commands sql #download free sql full book pdf #introduction to sql pdf #introduction to sql ppt #introduction to sql #practical sql pdf #sql commands pdf with examples free download #sql commands #sql free bool download #sql guide #sql language #sql pdf #sql ppt #sql programming language #sql tutorial for beginners #sql tutorial pdf #sql #structured query language pdf #structured query language ppt #structured query language
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Currently, the demand for the skill of SQL is on the rise. Most of the jobs describe their skill requirements, and while doing that, they mention the knowledge in SQL specifically. As the name suggests, ‘Data Science’ is data-driven. Thus, SQL will be an integral part of any data science job. It is also because of the advantage that it offers among the other alternatives. This article tries to elaborate on why SQL and querying are essential for data science and related roles.
If you want to learn SQL for data science, then you can start your journey here!
Structured Query Language, acronymized to SQL, is a computer programming language aimed and designed to manipulate data warehoused in RDBMSs, i.e., Relational Database Management Systems. Different functions such as insertion, deletion, updating, modification of data can be done using SQL. Since most of the structured data is stored in RDBMSs, working with data science will necessarily involve RDBMS and, hence, SQL.
With the advent of big data, data warehousing using relational database management systems has gained more importance, and it is strictly necessary to use them. Moreover, traditionally along with the programming languages Python and R, SQL is used. For instance, a data scientist can write an SQL query to extract data from a database, on which further analyses can be made using Python or R.
If you want to become a data scientist, then you can start your journey here!
Data Science is simply the analysis and study of data to extract meaningful insights. SQL comes into the picture in two of the most critical steps of a data science cycle — Data Extraction, the pre-processing step, as mentioned in the introduction, and Machine Learning. Most of the database platforms are designed using SQL, as it has become a standard for database systems. Also, it is easy to communicate with databases with complex instructions and manipulate data.
Modern systems such as Hadoop, Spark use SQL to maintain relational database systems and to process structured data. Identification of suitable data sources and pre-processing are the key steps in any data analysis work. Since the data is stored in relational databases, querying to extract the data without copying the entire database is necessary as it saves time and is efficient. Hence, a data scientist needs to have comprehensive knowledge in querying language, SQL.
SQL is a comprehensive language with several functions, statements, and operators that pave the way to seamless data extraction. SQL has multiple reasons to assert its importance and relevance in data science. First of all, even though SQL has a wide range of tools available, learning them is not an arduous task, as the commands and queries in SQL are comparable to simple English. For example, consider the SQL query ‘select name, nationality from employee’, which can be comprehended by any person of its function with its simplicity of language. Thus, a data science novice can quickly learn SQL, unlike the other programming languages that require more conceptual understanding.
#data-analysis #data #data-science #sql #data-visualization #sql
This article will introduce the concept of SQL recursive. Recursive CTE is a really cool. We will see that it can often simplify our code, and avoid a cascade of SQL queries!
The recursive queries are used to query hierarchical data. It avoids a cascade of SQL queries, you can only do one query to retrieve the hierarchical data.
First, what is a CTE? A CTE (Common Table Expression) is a temporary named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. For example, you can use CTE when, in a query, you will use the same subquery more than once.
A recursive CTE is one having a subquery that refers to its own name!
Recursive CTE is defined in the SQL standard.
A recursive CTE has this structure:
In this example, we use hierarchical data. Each row can have zero or one parent. And it parent can also have a parent etc.
Create table test (id integer, parent_id integer); insert into test (id, parent_id) values (1, null); insert into test (id, parent_id) values (11, 1); insert into test (id, parent_id) values (111, 11); insert into test (id, parent_id) values (112, 11); insert into test (id, parent_id) values (12, 1); insert into test (id, parent_id) values (121, 12);
For example, the row with id 111 has as ancestors: 11 and 1.
Before knowing the recursive CTE, I was doing several queries to get all the ancestors of a row.
For example, to retrieve all the ancestors of the row with id 111.
While (has parent) Select id, parent_id from test where id = X
With recursive CTE, we can retrieve all ancestors of a row with only one SQL query :)
WITH RECURSIVE cte_test AS ( SELECT id, parent_id FROM test WHERE id = 111 UNION SELECT test.id, test.parent_id FROM test JOIN cte_test ON cte_test.id = test.parent_id
) SELECT * FROM cte_test
It indicates we will make recursive
It is the initial query.
It is the recursive expression! We make a jointure with the current CTE!
Replay this example here
#sql #database #sql-server #sql-injection #writing-sql-queries #sql-beginner-tips #better-sql-querying-tips #sql-top-story