Introduction to the Concept of Data Warehouse

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We construct a Data Warehouse by integrating data from various sources. This process supports analytical reporting, structured and unstructured queries, and organizational decision-making. We follow a step-by-step approach to build and use a Data Warehouse effectively. Many data scientists get their data in raw formats from various sources of data and information. But, for many data scientists also as business decision-makers, particularly in big enterprises, the main sources of data and information are corporate data warehouses. A data warehouse holds data from multiple sources, including internal databases and Software (SaaS) platforms. After we load the data, we often cleanse, transform, and check it for quality before using it for analytics reporting, data science, machine learning, or other purposes.

This article was published as a part of the Data Science Blogathon

What is Data Warehouse?

A Data Warehouse is a collection of software tools that facilitates analysis of a large set of business data used to help an organization make decisions. A large amount of data in data warehouses comes from numerous sources such that internal applications like marketing, sales, and finance; customer-facing apps; and external partner systems, among others. It is a centralized data repository for analysts that can be queried whenever required for business benefits. We construct a Data Warehouse by integrating data from various sources. This process supports analytical reporting, structured and unstructured queries, and organizational decision-making. We follow a step-by-step approach to build and use a Data Warehouse effectively.

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What is Data Warehousing?

The process of creating data warehouses to store a large amount of data is named Data Warehousing. Data Warehousing helps to improve the speed and efficiency of accessing different data sets and makes it easier for company decision-makers to obtain insights that will help the business and promoting marketing tactics that set them aside from their competitors. We can say that it is a blend of technologies and components which aids the strategic use of data and information. The main goal of data warehousing is to create a hoarded wealth of historical data that can be retrieved and analyzed to supply helpful insight into the organization’s operations.

Need of Data Warehousing.

Data Warehousing is a progressively essential tool for business intelligence. It allows organizations to make quality business decisions. The data warehouse benefits by improving data analytics, it also helps to gain considerable revenue and the strength to compete more strategically in the market. By efficiently providing systematic, contextual data to the business intelligence tool of an organization, the data warehouses can find out more practical business strategies.

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1. Business User: Business users or customers need a data warehouse to look at summarized data from the past. Since these people come from a non-technical background, we should represent the data to them in an uncomplicated way.
2. Maintains consistency: We program data warehouses to apply a regular format to all collected data from different sources. This makes it effortless for company decision-makers to analyze and share data insights with their colleagues around the globe. By standardizing the data, we reduce the risk of error in interpretation and improve overall accuracy.
3. Store historical data: Data Warehouses are also used to store historical data that means, the time variable data from the past and this input can be used for various purposes.
4. Make strategic decisions: Data warehouses contribute to making better strategic decisions. Some business strategies may be depending upon the data stored within the data
   warehouses.
5. High response time: Data warehouse has got to be prepared for somewhat sudden masses and type of queries that demands a major degree of flexibility and fast latency.

Characteristics of Data warehouse:

1. Subject Oriented:A data warehouse is often subject-oriented because we design it to deliver insights on a particular theme. This means we propose the data warehousing process to handle a specific, well-defined theme. These themes are often sales, distribution, selling. etc.
2. Time-Variant: When we maintain data over different intervals of time, such as weekly, monthly, or annually, we establish numerous time limits. These limits are structured between large datasets and are managed within the online transaction processing (OLTP) method. We extend the time limits for the data warehouse beyond those of operational systems. We store data in the data warehouse with a predetermined interval of time and deliver information from a historical perspective. It contains parts of time directly or indirectly.
3. Non-volatile: The data residing in the data warehouse is permanent and defined by its names.It also means that we cannot erase or delete data in the data warehouse, nor can we insert new data into it freely. In the data warehouse, we treat data as read-only and refresh it only at specific intervals. When we perform operations like delete, update, or insert in a software application, we lose these changes in the data warehouse environment. We can only perform two types of data operations in the data warehouse:

• Data Loading
• Data Access

4. Integrated: A data warehouse is created by integrating data from numerous different sources such that from mainframe computers and a relational database. Additionally, it should also have reliable naming conventions, formats, and codes. Integration of data warehouse benefits in the successful analysis of data. Dependability in naming conventions, column scaling, encoding structure, etc. needs to be confirmed. Integration of data warehouse handles numerous subject-oriented warehouses.

Architecture & Components of Data Warehouse:

Data warehouse architecture defines the comprehensive architecture of data processing and presentation that will be useful for data analysis and decision making within the enterprise and organization. Each organization has different data warehouses based on their needs, and we characterize all of them by certain standard components.

Data Warehouse applications are designed to support the user’s data requirements, an example of this is online analytical processing (OLAP). These include functions such as forecasting, profiling, summary reporting, and trend analysis.

The architecture of the data warehouse mainly consists of the proper arrangement of its elements, to build an efficient data warehouse with software and hardware components. The elements and components may vary based on the requirement of organizations. All of these depend on the organization’s circumstances.

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1. Source Data Component:

In the Data Warehouse, the source data comes from different places. They are group into four categories:

• External Data: For data gathering, most of the executives and data analysts rely on information coming from external sources for a numerous amount of the information they use. They use statistical features associated with their organization, which they obtain from external sources and departments.
• Internal Data: In every organization, the consumer keeps their “private” spreadsheets, reports, client profiles, and generally even department databases. This is often the interior information, a part that might be helpful in every data warehouse.
• Operational System data: Operational systems are principally meant to run the business. In each operation system, we periodically take the old data and store it in achieved files.
• Flat files: A flat file is nothing but a text database that stores data in a plain text format. Flat files generally are text files that have all data processing and structure markup removed. A flat file contains a table with a single record per line.

2. Data Staging:

After we extract the data from various sources, it’s time for us to prepare the data files for storage in the data warehouse. We must transform the extracted data collected from various sources and format it so that it is suitable for saving in the data warehouse for querying and analysis.The data staging contains three primary functions that take place in this part:

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• Data Extraction: This stage handles various data sources. Data analysts should employ suitable techniques for every data source.
• Data Transformation: As we all know, information for a knowledge warehouse comes from many alternative sources. If information extraction for a data warehouse posture huge challenges, information transformation gifts even important challenges. We tend to perform many individual tasks as a part of information transformation. First, we tend to clean the info extracted from every source of data. Standardization of information elements forms an outsized part of data transformation. Data transformation contains several kinds of combining items of information from totally different sources. Information transformation additionally contains purging supply information that’s not helpful and separating outsourced records into new mixtures. Once the data transformation performs ends, we’ve got a set of integrated information that’s clean, standardized, and summarized.
• Data Loading: When we complete the structure and construction of the data warehouse and go live for the first time, we do the initial loading of the data into the data warehouse storage. The initial load moves high volumes of data consuming a considerable amount of time.

3. Data Storage in Warehouse:

Data storage for data warehousing is split into multiple repositories. These data repositories contain structured data in a very highly normalized form for fast and efficient processing.

• Metadata: Metadata means data about data i.e. it summarizes basic details regarding data, creating findings & operating with explicit instances of data. We create metadata either manually by making additional corrections or automatically. This metadata contains basic information about the data.
• Raw Data: Raw data is a set of data and information that we have not yet processed. We deliver it from a specific data entity to the data supplier, and neither machines nor humans have processed it yet. We gather this data from online sources to provide deep insights into users’ online behavior.
• Summary Data or Data summary: Data summary is an easy term for a brief conclusion of an enormous theory or a paragraph. This is often one thing where analysts write the code and in the end, they declare the ultimate end in the form of summarizing data. Data summary is the most essential thing in data mining and processing.

4. Data Marts:

Data marts are also the part of storage component in a data warehouse. It can store the information of a specific function of an organization that is handled by a single authority. There may be any number of data marts in a particular organization depending upon the functions. In short, data marts contain subsets of the data stored in data warehouses.

Now, the users and analysts can use data for various applications like reporting, analyzing, mining, etc. The data is made available to them whenever required.

Data Warehousing life Cycle:

As we know, we create a data warehouse by combining data from multiple diverse sources. We use tools that support analytical reporting, structured and unstructured queries, and decision-making for the organization. We need to follow the step by step approach for building and successfully implementing the Data Warehouse:

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How does Data Warehouse work?

A Data Warehouse is like a central depository where data comes from different data sources. In a data warehouse, the data flows from the transactional system and relational databases. A data warehouse timely pulls out the data from various apps and systems, after then, the data goes through various processing and formatting and makes the data in a format that matches the data already in the warehouse. This processed data is stored in the data warehouses that ready for further analysis for decision making. The data formatting and processing depends upon the need of the organization

The Data could be in one of the following formats:

1. Structured
2. Semi-structured
3. Unstructured data

The process and transform the data so that users and analysts can access the processed data in the Data Warehouse using Business Intelligence tools, SQL clients, and spreadsheets. A data warehouse merges all information coming from various sources into one global and complete database. By merging all of this information in one place, it becomes easier for an organization to analyze its customers more comprehensively.

Latest Tools and Technologies for Data Warehousing:

Data warehousing had improved the access to information, reduced query-response time, and also allows businesses to get deep insights from huge big data. Earlier, companies had to build lots of infrastructure for data warehousing. But today the cloud technology has remarkably reduced the cost and effort of data warehousing for businesses.

The field of data warehousing is rapidly emerging, and we are developing various cloud data warehousing tools and technologies to enhance decision-making. The cloud-based data warehousing tools are fast, highly scalable, and available on a pay-per-use basis. Following are some data warehousing tools:

1. Amazon Redshift
2. Microsoft Azure
3. Google BigQuery
4. Snowflake
5. Micro Focus Vertica
6. Teradata
7. Amazon DynamoDB
8. PostgreSQL
9. Amazon RD
10. Amazon S3

All these are the top 10 Data Warehousing Tools. In this article, we are going to use Google BigQuery for data warehousing.

Getting Started with Google BigQuery:

Google BigQuery is a highly scalable, cost-effective and server-less data warehouse mainly designed for quickly and easily moving businesses. Google BigQuery is a cloud-based enterprise data warehouse that provides supports to rapid SQL queries and interactive analysis of Big datasets. So, let’s get started:

[Source]

Before we begin

We must use the Cloud Resource Manager to Create a Cloud Platform project if you do not already have one, enable billing for the project and then enable BigQuery APIs for the project.

Now, Provide your credentials to the runtime

from google.colab import auth
auth.authenticate_user()
print('Authenticated')

Enable data table display

Colab includes the google.colab.data_table package, which we can use to display large pandas DataFrames as interactive data tables. We enable this feature with:

%load_ext google.colab.data_table

If we want to return the classic Pandas dataframe display, we can disable this by running:

%unload_ext google.colab.data_table

Use BigQuery via magics

The google.cloud.bigquery library also includes a magic command which runs a query and either displays the result or saves it to a variable as a DataFrame.

# Display query output immediately

%%bigquery --project yourprojectid

SELECT 
 COUNT(*) as total_rows
FROM `bigquery-public-data.samples.gsod`

# Save output in a variable `df`

%%bigquery --project yourprojectid df

SELECT 
 COUNT(*) as total_rows
FROM `bigquery-public-data.samples.gsod`
df

Use BigQuery through google-cloud-bigquery  and declare the Cloud project ID which will be used throughout this notebook:

project_id = '[your project ID]'

Sample approximately 2000 random rows

from google.cloud import bigquery
client = bigquery.Client(project=project_id)

sample_count = 2000

row_count = client.query('''

 SELECT 

 COUNT(*) as total

 FROM `bigquery-public-data.samples.gsod`''').to_dataframe().total[0]

df = client.query('''

 SELECT

 *

 FROM

 `bigquery-public-data.samples.gsod`

 WHERE RAND() < %d/%d

''' % (sample_count, row_count)).to_dataframe()
print('Full dataset has %d rows' % row_count)

Describe the sampled data

df.describe()

View the first 10 rows

df.head(10)

Use BigQuery through pandas-gbq

The pandas-gbq library is a community-led project by the pandas community. It covers basic functionality, such as writing a DataFrame to BigQuery and running a query, but as a third-party library, it may not handle all BigQuery features or use cases.

import pandas as pd

sample_count = 2000
df = pd.io.gbq.read_gbq('''

 SELECT name, SUM(number) as count

 FROM `bigquery-public-data.usa_names.usa_1910_2013`

 WHERE state = 'TX'
 GROUP BY name
 ORDER BY count DESC
 LIMIT 100
 ''', project_id=project_id, dialect='standard')


df.head()

Syntax highlighting

from google.colab import syntax
query = syntax.sql('''
SELECT

 COUNT(*) as total_rowsFROM

 `bigquery-public-data.samples.gsod`

''')

pd.io.gbq.read_gbq(query, project_id=project_id, dialect='standard')

This brings us an end to this article. Hope you enjoyed reading the article.

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