Amya  Gleichner

Amya Gleichner

1603433860

A Complete Beginner’s Guide to Data Warehousing

Before we go any further, I need to share something critical that I learned while talking to Ahmed about the entire data world. Definitions won’t get you anywhere. You need to understand the problems that are present in the data warehousing space and address them and knowing just knowing what a star schema is won’t help you do that.

So just wanna learn some definitions and basic ideas? Read Part 1.

For a more in-depth look at the issues currently with the data warehousing field and how to shift your mindset to become better at analysis, go to Part 2. Or do both!

Part 1: The Bare Bone Basics

Data Warehousing: storing and connecting all crucial business/company data in a single space

The main purpose of organizing data in a go-to, easy-to-access way is so that it can be analyzed later on. Or in fancier terms, it should be able to support Business Intelligence (BI) actions later on, with the most important aspect being analysis. There’s no point in having huge amounts of data if it’s super messy and can’t be analyzed for optimization! That’s where the two most popular data warehousing models come in, star schema and snowflake schema.

Star Schema: a data warehousing model that consists of fact and dimension tables, where the dimension tables are denormalized

Let’s unpack that definition. In this model, you have pieces of data called measures and others called dimensions. The measures are numerical data like the number of units sold or the number of emails opened. Dimensions give context to the measures. The dimensions for the aforementioned examples could be UnitSales and OpenedEmails, respectively.

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An example of a star schema. By SqlPac at English Wikipedia, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=76901169

These measures are stored in fact tables and dimensions are stored in dimension tables. The fact tables have dimension key columns (that relate them to the dimension tables) and numeric measure columns, which store the measures.

Fact tables come in 3 distinct flavours.

  1. Transaction fact tables: these store data about a specific, one-time event so the data is only stored once
  2. (Periodic) snapshot fact tables: as the name suggests, these store snapshot data or data at a given point in time, like the number of purchases per customer at the end of the quarter. This means the data is stored multiple times at regular intervals.
  3. Accumulating snapshot tables: and these store accumulating data, or data over time, like the total number of units of a certain product sold over time

Dimension tables can vary a lot across industries and companies but here are some of the most popular types.

  1. Time dimension tables: these store the time at which the related measure in the corresponding fact table was true
  2. Geography dimension tables: these store, you guessed it, geographical data like city, country, postal/zip code, etc.
  3. Product dimension tables: these store information about the product like the productID, product name, and brand name
  4. Employee dimension tables: these store data about the employees like employeeID, employee name, address, etc.
  5. Range dimension tables: these store ranges of data like a time period or a range in price, which can help report later on

This is cool and all but what the denormalized thing from the definition before mean? Your data can be normalized or denormalized. Normalized data is when the data’s stored in multiple tables with relationships linking them to each other. It helps maintain more of the data’s integrity and reduces redundancy but it slows things down since the computer has to jump from one table to another, multiple times. Denormalized data which is how star schema’s organized is when everything’s organized into one main table along with some other dimension tables. The real difference is that there are only direct links to the fact table. However, normalized data can have multiple links.

Things should be clicking into place now as to why this model is called star schema. It’s because it looks like a star with a central fact table and dimension tables “fanning out” from it.

Snowflake Schema: a multidimensional data warehousing model that consists of fact and dimension tables, where the dimension tables are normalized

It’s helpful to think of a snowflake schema as having the same so-called base as a star schema. It also has a central fact table and dimension tables connected to it. However, to reduce redundancy, optimize storage, and generally clean things up, snowflake schemas have dimension tables connected to their first layer of dimension tables.

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A “template” for a snowflake schema. By SqlPac — Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=4285113

Don’t be scared off by the jargon. Here’s how Ahmed would explain a star schema to a 7-year-old!

Imagine having a bunch of excel sheets with information in columns. And you have a laser pointer that can connect the information in one table to the others.

#data-analysis #developer

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A Complete Beginner’s Guide to Data Warehousing
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

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

Lisa joly

Lisa joly

1624089840

Big Data Resume: Complete Guide & Samples [2021]

Thanks to the rapidly piling amounts of Big Data, the job profile of a Big Data Engineer is peaking.

In recent years, there has been such unprecedented growth in the demand for Big Data Engineers that it has become one of the top-ranking jobs in Data Science today. Since numerous companies across different industries are hiring Big Data Engineers, there’s never been a better time than now to build a career in Big Data. However, you must know how to present yourself as different from the others; you need to stand out from the crowd. Read the blog to have a better understanding of the scope of Big Data in India.

And how will you do that?

By designing and crafting a detailed, well-structured, and eye-catching Big Data resume!

When applying for a Big Data job, or rather for the post of a Big Data Engineer, your resume is the first point of contact between you and your potential employer. If your resume impresses an employer, you will be summoned for a personal interview. So, the key is to make sure you have a fantastic resume that can get you job interview calls.

Usually, Hiring Managers have to look at hundreds of resumes, be it for any job profile. However, when it comes to high-profile jobs like that of the Big Data Engineer, you must be able to grab the attention of the Hiring Manager by highlighting your skills, qualifications, certifications, and your willingness to upskill.

Let’s begin the resume-building process with the job description and key roles and responsibilities of a Big Data Engineer.

Table of Contents

#big data #big data resume: complete guide & samples #big data resume #big data resume #data science resume #guide

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

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.

Introduction

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

Tia  Gottlieb

Tia Gottlieb

1594236360

A Beginner’s Guide To Cleaning Data In R — Part 1

My journey into the vast world of data has been a fun and enthralling ride. I have been glued to my courses, waiting to finish one so I can proceed to the next. After completing introductory courses, I made my way over to data cleaning. It is no secret that most of the effort in any data science project goes into cleaning the data set and tidying it up for analysis. Therefore, it is crucial to have substantial knowledge about this topic.

Firstly, to understand the need for clean data, we need to look at the workflow for a typical data science project. Data is first accessed, followed by manipulation and analysis of the data. Afterward, insights are extracted, and finally, visualized and reported.

Accessing data, followed by analysis, insight generation and visualisation.

Typical Project Workflow

Errors and mistakes in data, if present, could end up generating errors throughout the entire workflow. Ultimately, the insights generated that are used to make critical business decisions are incorrect, which may lead to monetary and business losses. Thus, if untidy data is not tackled and corrected in the first step, the compounding effect can be immense.

This guide will serve as a quick onboarding tool for data cleaning by compiling all the necessary functions and actions that should be taken. I will briefly describe three types of common data errors and then explain how these can be identified in data sets and corrected. I will also be introducing some powerful cleaning and manipulation libraries including dplyr, stringr, and assertive. These can be installed by simply writing the following code in RStudio:

install.packages("tidyverse")
install.packages("assertive")

1. Incorrect Data Type

When data is imported, a possibility exists that RStudio incorrectly interprets a data column type, or the data column was wrongly labeled during extraction. For example, a common error is when numeric data containing numbers are improperly identified and labeled as a character type.

a) Identification

Firstly, to identify incorrect data type errors, the glimpse function is used to check the data types of all columns. The glimpse function is part of the **dplyr **package which needs to be installed before glimpse can be used. Glimpse will return all the columns with their respective data types.

library(dplyr)
glimpse(dataset)

Another form of logical checks includes the is function. The is function can be used for each data type and will return with a logical output (true/false). I have only mentioned the common is functions, but it can be used for all data types. If a numeric column is an argument for the is.numeric function, the output will be true, while if a character column is an argument for the is.numeric function the output will be false.

is.numeric(column_name)
is.character(column_name)

b) Correction

After all the incorrect data type columns have been identified, they can simply be converted to the correct data type by using the as functions. For example, if a numeric data type has been incorrectly imported as a character data type, the as.numeric function will convert it to numeric data type.

as.numeric(column_name)

#data-analysis #data-scientist #data #data-cleaning #r #data analysis

Cyrus  Kreiger

Cyrus Kreiger

1618039260

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt