What is data entry?

1.What is data entry?

A quick guide to data entry

Business moves at a lightning-fast pace, and as your brand grows, it can be harder to keep up with all the information that you're collecting at every turn — this is where data entry can be of benefit. It can help create new or update customer accounts, transcribe data from important audio files (including recordings of live phone conversations), or upload expenses. Data entry can be your business lifesaver.

Working with data entry clerks can help your team focus on mission-critical tasks while still keeping all of your databases accurate and up-to-date. This helps your business use data to its advantage and improve your overall operational workflow.

In this guide, we are going to give you a simple explanation of what actually is data entry, and what are a data entry clerk's responsibilities.

Data entry explained

1. What is data entry?

Data entry is a type of clerical work involving processes like voice recording and typing to enter raw data into computer systems and databases, including the addition of new data or updating existing data.

This can include transcribing notes from meetings or important calls, uploading customer information from paper records to computer records, inputting expenses into financial software, or even adding sales figures into electronic formats. It's all about collecting, managing, and keeping track of the data your business needs.

Data entry specialists can input information into spreadsheets, word documents, or the company's software. They may also be responsible for scanning documents. There are plenty of different types of data entry jobs, which can include "coders," "typists," "transcribers," and "word processors," in addition to straightforward "data entry" positions. They can be hourly or salaried employees, but they can also be contractors who are paid by the project or by keystrokes per word, keywords per minute, or keywords per hour.

2. Types of data entry jobs

There are several different types of data entry jobs that organizations commonly hire for. The titles and job roles may vary based on the employer, the company's industry, and a brand's specific needs. Some of the most popular types of data entry job titles include the following:

Data entry clerks, where the job involves inputting key information from one format to another, often through typing

Data entry keyer, in which the employee enters or scans data into computer software systems, often from hard copies or physical documents

Transcriptionists, where the job typically involves listening to audio or video recordings and transcribing the information into text format; this is word-focused instead of centered around numerical data

Word processors & typists, where the primary focus is typing to enter information from text documents, including correspondence, general data, and reports

3. What is a data entry clerk?

A data entry clerk is someone who is employed either as an employee or a third-party contractor to handle administrative work and routine clerical tasks to update or add data into a computer system or database. Their primary responsibility, aside from data entry, is to ensure data accuracy.

This may involve entering data from hard copies and paper documents into the database, typically using a keyboard. Data entry clerks often use keyboards specifically designed for data entry, which have special keys and colors to help increase speed. Many of these keyboards are also created with ergonomics in mind since data entry specialists often spend so much time working at a keyboard.

Speed and accuracy are important traits that data entry specialists should have, as they can increase the output of the specialist while ensuring that the data is all correct.

Find a data entry clerk for hire

4. What does a data entry clerk do?

Data entry clerks handle the routine data entry and clerical tasks that involve updating or entering new data into the company's databases.

A core responsibility is to ensure data accuracy. They need to ensure that a company's existing database has accurate and up-to-date information so that the company can act on the right data. It's also common for data entry clerks to help with the finance department on tasks like accounts receivable, processing payments, and reviewing and processing order returns.

There are other common responsibilities that data entry clerks may have, depending on their specific job role and the company that hired them. These include the following:

Collecting and inputting data into the company's databases

Maintaining a database by updating information on an ongoing basis, including entering customer data in alphanumeric calculators with a keyboard or optical scanner

Reviewing customer and business records for accuracy, comparing it to new data that's been acquired, and editing, adding, or deleting data as needed

Performing backups and downloads of information for data preservation purposes

Retrieving records and files from the database to present to other team members as needed

Organizing paper records or notes after data entry for storage, archives, or shredding

Organizing digital records according to a client's standards

Entering account or customer information when new information is provided to the business

Maintaining all company policies, data entry techniques, and procedures in regards to data uploading

Adhering to deadlines as set by the organization

Scanning documents and files and uploading them into the business's databases or archives

Transcribing audio or video into text-based documents

Assisting with building data management systems or reviewing current data management systems

Generating reports based on the data compiled

Using word processing, spreadsheet, database, or other computer software systems in addition to office equipment like special keyboards, printers, and scanners

May be asked to tackle other administrative work depending on the company and the clerk's specific experience

Preparing source data for database entry by compiling and organizing the information

Establishing data entry priorities and tasks

Secures data by processing database backups and ensuring that the backups are functional

Purging files to eliminate duplication of data, such as duplicate customer accounts, and combining data from multiple sources in a single database

Tests account and customer system changes and upgrades by inputting new data

Contributing to teamwork by taking on other related tasks as needed by an organization

Keeps all data confidential, including business information and customer data

Conclusion

While data entry can fall by the wayside when your staff is overwhelmed, it's one thing that should never be neglected. You need strong, accurate, and updated information for every part of your organization. This will help you make stronger decisions that will lead your business to more success, instead of basing important business decisions on inaccurate data.

Before you start looking for a data entry specialist, think about what tasks you'll need them to tackle and what roles you'd like them to take. Determine if you'd like them to be in-office or remote, and what your desired budget is. This will help you determine the best approach.

2. What is data visualization?

Data visualization definition and examples

Data visualization was born out of the world's fascination with Big Data, along with the treasure trove of information it provides to businesses. While no one can deny the value of data, the truth is that it's not quite as effective without the simple yet powerful practice of data visualization.

So, what exactly is data visualization, and why is it so vital for businesses both big and small? To help you understand these questions, we've written this in-depth guide about all there is to know about data visualization, including its benefits, different types, best practices, and more.

Data Visualization: everything you need to know

1. What is data visualization?

Data visualization is the representation of data and information through visual elements, such as graphs, charts, maps, and other graphical images.

In our current digital age that’s dominated by Big Data, data visualization tools and technologies play a key role in analyzing large amounts of information and making more informative and data-backed decisions.

This is because they provide a more accessible method for viewing and understanding sets of data, including its trends, outliers, and other helpful patterns.

2. Why is data visualization important?

Data visualization is used across all industries and fields, as its primary goal of delivering data in the most effective way is something that benefits businesses of all sizes. In its very essence, data visualization takes raw data, which is then modeled and delivered in an easy-to-understand and visual manner, so conclusions can be reached.

The importance of data visualization is only growing, leading data scientists to develop machine learning algorithms that assemble crucial data into visualizations that are easier to understand. By displaying data in a visual manner, the information is conveyed in a way that is not only fast and effective but universally recognized too.

Data visualization is a crucial step in the business intelligence process. It assists businesses to recognize which areas need to be improved, which factors affect how their customers think about their brand, and the appropriate course of action for each product — including who they should be sold to and where.

Additionally, data visualization gives executives, business owners, and other stakeholders a more thorough and data-backed prediction of future sales and growth.

3. The benefits of data visualization

Now that you’ve learned about the importance of data visualization, let’s take a closer look at the specific benefits it offers in terms of improving data insights.

Better analysis. Data visualization assists business executives to analyze their reports easier and more effectively, particularly those involving sales, marketing initiatives, and product popularity. Through better analysis, stakeholders can focus on the specific areas which will allow them to boost their profits, thus making their business more successful.

Quick action. Earlier, we mentioned how our brains are able to understand information conveyed visually through graphs, rather than reports. This is another big advantage of data visualization, as it allows businesses to recognize significant changes in their data quicker and take necessary actions much sooner too. This, in turn, leads to faster business growth.

Identifying patterns. Data visualization also encourages stakeholders to easily identify patterns or consistencies among data. Through recognizing these relationships, it provides greater meaning and can also highlight which areas require further attention. When this significance is identified, it's a lot easier to focus on how these patterns contribute to business growth.

Finding errors. While identifying patterns or changes in data is crucial to a business, so too is finding errors. Incorrect data can cause an organization to implement incorrect strategies or actions. Visualizations can help businesses find these errors faster, allowing them to remove the incorrect data from their analysis before it’s too late.

Understanding the story. Data visualization also plays a key role in storytelling. When your visuals are designed in a clear, simple, and effective way, they allow your target audience to understand the narrative you’re conveying in just a single glance. Just keep in mind that complicated or excessive data visualizations will have the opposite effect.

Exploring business insights. The current business environment is extremely competitive, however, those who look for correlations in their data to uncover key business insights place themselves at an advantage over those who don’t. This is because doing so allows organizations to set the right path to achieving their specific goals.

Grasping the latest trends. Data visualization also allows you to discover rising trends that are applicable to your business or industry. This helps you capitalize on these trends while the timing is right and boost your profits. Trends among data may also signal a looming issue, so can help a business to identify problems before they arise.

4. Types of data visualization

Did you know there are many different types of data visualization? Let's explore the 5 most popular types here below, along with how you can use them and what types of businesses they work best for.

1. Column charts

Due to their ease of creation, column charts (also known as column graphs) have become the most popular data visualization method. In fact, they’re one of the earliest data visualization methods taught at school as a way for students to learn, understand, and analyze data. Typically, a column chart will present and compare a specific topic, including how many are present in each group.

Column charts are made up of horizontal and vertical columns, each of which represents different sets of data. These columns are displayed on an x- and y-axis. Most commonly, vertical bars are used along the x-axis to represent the data, while a numbered scale along the y-axis displays the measurements involved to provide context. In some cases, however, it’s more suitable to switch the axes around to use horizontal (rather than vertical) bars. This is referred to as a bar chart or bar graph.

Multiple sets of data can also be represented in the same column or bar graph by stacking columns. This would be helpful if comparing similar information, such as the occurrence of an event across every day of the week — including day or night — for example.

When designing a column chart, ensure each set of data is differentiated using labels or colors and choose a scale that works best for the data you must represent. To do this, take both the lowest and highest numbers into consideration. For example, there’s no need to use a scale for your graph that goes up to 100 if the highest number you need to record is 63. Capping the graph at 65, therefore, would work best.

When to use a Column chart?

Column charts are most effective at displaying differences between sets of data or how one set of data has changed over time. You could therefore use a column chart to show the popularity of your products, sales made per month, website visitors, audience demographics, or projection of future demand.

2. Line graphs

Line graphs are another popular data visualization choice, particularly because they’re simple and versatile enough to meet a wide range of purposes. Line graphs are good at displaying changes over time, including those projected for the future. A downward slope, for example, showcases a decline, whereas an upward slope represents an increase.

Line graphs are also displayed on an x- and y-axis. The x-axis shows the time range, while the y-axis conveys the measurements being tracked. Each data set is represented using a solid line, and multiple lines can be included within a single graph. Additionally, a legend or labels should be used to identify each individual line.

The most effective way to differentiate each data set is through displaying the lines in different colors, then including a legend to specify what each color represents. Try to limit your charts to four lines or less, however, to avoid confusing viewers. Additionally, you should also choose a measurement range that works best for both the lowest and highest numbers being recorded.

When to use a line graph?

Businesses might find line graphs helpful for tracking how their hours worked, expenses, or profits compare to previous time periods. Additionally, line graphs can help an organization record and analyze future projections, thus assisting them to plan their next course of action.

3. Matrix diagrams

A matrix diagram compares groups of data within a larger category of information, therefore it’s a great method for visually exploring how this data interacts with and influences each other.

There are five different types of matrix diagrams that you might consider using, depending on your needs. These include:

L-shaped: for comparing two groups to each other or one data set to itself

Y-shaped: for comparing three closely related groups to one another

C-shaped: for comparing three groups at the same time

T-shaped: for comparing two closely related groups together

X-shaped: for comparing four different groups to one another

Before you begin designing your matrix diagram, you’ll first want to determine how you should compare the datasets or relationships you’re examining. To prevent confusion, ensure you assign a symbol to each set of data to keep track of the right numbers in the diagram.

When to use a matrix diagram?

Matrix diagrams are especially helpful for organizations focusing on causation. This is because they allow you to not only identify the root of any problem but also to weigh up multiple solutions. Business owners can use matrix diagrams to determine if their teams are meeting what’s required of them. Additionally, data presented in this manner can help you to find opportunities to improve processes.

4. Scatter plot charts

Scatter plot charts — or scattergrams — use a large number of varied data points to find relationships between data sets. Scatter plot charts also use both an x- and y-axis, and not only show similarities between points, but also highlight outliers.

For a scatter plot chart to be effective, each variable being plotted requires several data points. This helps you to discover the pattern — if there is one — to determine the relationship between the data. An upward trend in data from left to right indicates an increase, for example, and reveals valuable insight. Likewise, a downward trend in data points tells of a decline.

A scatter plot chart’s purpose is to help you conclude “when x equals this, then y equals that.” For the most effective results, you should select variables that have a higher chance of affecting each other. Furthermore, it can be easy to think all trends in data are equal to causation. Be sure to continue monitoring this data if a pattern is forming so you can determine if another variable may also be influencing these outcomes.

When to use a scatter plot chart?

Scatter plot charts identify links between data; therefore, they can be used for basically every business scenario required. If your business relies heavily on the delivery of orders, for example, then a scatter plot chart could compare how long it takes to make a delivery at each hour throughout the day to determine any trends. The findings could then reveal if the time of day affects a driver’s efficiency. This information could also be used to discover a solution to this problem, like performing deliveries during certain hours only, for example.

5. Pie charts

Pie charts are circular in appearance and feature multiple ‘slices’ or segments to show how the topic being examined is made up of various percentages. It visually shows how parts of the data set compare to others.

Pie charts give you key information about a subject at-a-glance, rather than deep analytical insights. Therefore, pie charts are best reserved for data that has been broken down into smaller percentages of a larger whole. Start by confirming all portions add up to 100%. Then, ensure each portion has been clearly labeled with its corresponding percentage to help your audience understand the breakdown.

While labels are necessary, so is using different colors to separate each segment. For a more effective pie chart design, you should also organize the slices according to their size, so your data visualization conveys the information more effectively. Finally, it’s important not to have too many segments in your pie chart, otherwise, it will not be as visually effective. Data requiring more than five slices in your chart would be conveyed better through another visualization method.

When to use a pie chart?

Pie charts are perfect for examining a larger issue and breaking it down into smaller components. For example, businesses can benefit from using a pie chart to examine how they allocate resources, which products are more popular in terms of sales, or which demographics make up the largest portion of their target audience. With this knowledge, your business can adjust relevant processes to increase efficiency and profits.

5. Data visualization tools for business

We’ve explored the many different data visualization types, so now let's turn our focus to the vital data visualization tools for your business.

A data visualization tool is a type of software that assists you to visualize data sets. It does this by allowing you to input data then convey it in a visual format. Each data visualization tool will vary in its features and functionality; however, most have pre-existing templates that can be used to produce quick and easy-to-understand visualizations.

Let’s take a closer look at the top data visualization tools for business.

1. Microsoft Excel (and Power BI)

Although Microsoft Excel is a spreadsheet software — rather than an exclusive data visualization tool — it does have valuable data visualization capabilities. Another benefit of a Microsoft product such as Excel is that it is likely already available to your corporation.

Excel allows you to turn your spreadsheet data into at least 20 different types of visualizations, including column charts, line graphs, scatter plots, pie charts, and more. There are limitations to Excel’s capabilities, however, so those looking for a more advanced data visualization tool should consider Power BI.

Power BI is another Microsoft product, however, it has been created specifically for data analytics and visualization. It lets you import data from a number of sources, then turns the information into a wide range of visualizations.

2. Google charts

If you’re wanting to create an interactive data visualization that will be viewed online, then Google Charts is a popular and free resource. It integrates with a number of sources — such as SQL databases, Salesforce, or Google Sheets — pulls the data, then uses HTML5/SVG technology to generate charts. There are 18 different types of visualizations available, including bar charts, pie charts, scattergrams, line graphs, and more.

Google Charts also has a community of users who share their own data visualization designs so others can use them. Although these charts may offer more advanced capabilities, they are sometimes not HTML5-compliant.

3. Tableau

Tableau is another popular data visualization tool for a number of reasons. Firstly, it’s quite simple to use, yet has powerful capabilities. Secondly, the software can integrate with hundreds of data sources to generate visualizations. Finally, Tableau is owned by Salesforce; therefore, it has a community of millions of users and is often used by large, global corporations.

Tableau also offers multiple versions of its analytics software, including desktop, server, and web-hosted versions, along with customer relationship management (CRM) software.

For those wishing to try the software first, Tableau has a free version of its data visualization software called Tableau Public. The downside to this is that any visualizations you create through the free version are made public. It’s a great way to learn the ropes of the popular paid software, however, businesses must keep this in mind if they don’t want certain information to become public.

4. Zoho analytics

Zoho Analytics has been created with a specific audience in mind: professionals wanting to create visualizations for business intelligence. This makes the software a great tool for working with data sets relating to sales, marketing, profit, revenues, costs, and more.

Zoho Analytics offers numerous packages to suit a user’s needs, including multiple paid versions, as well as a free edition. The free package is great for testing out the software, however, it does limit the number of data visualizations you can create.

Though popular, there are some additional tools that offer the same powerful data visualization capabilities to Zoho Analytics. These include HubSpot and Databox.

5. Datawrapper

Just like Google Charts, Datawrapper is designed with the creation of online data visualizations in mind. It’s available in both free and paid versions and allows users to turn their data into common visualizations such as scatter plot charts, pie charts, bar graphs, and more.

The tool is very easy to use, however it does lack some capabilities compared to the other tools we’ve discussed so far. One of these differences is that Datawrapper doesn’t integrate with external data sources. Therefore, you have to manually copy and paste data into the software, which can be both time-consuming and risky in terms of human error.

6. Infogram

The final data visualization tool on our list is Infogram, which is another popular application for turning data into charts, reports, maps, and more. What sets this tool apart from the others we’ve mentioned, however, is its ability to also produce infographics. This, along with its easy drag-and-drop capabilities, makes Infogram very popular among those working in creative industries.

The visualizations created in this tool can also be saved as image files and GIFs. These can then be embedded in documents or in HTML to be used online.

Infogram has a number of plans available to suit the needs of every business. There’s a free version with limited capabilities, all the way up to an enterprise-level plan.

The benefits of data visualization tools

Using data visualization tools to convey data in a visual manner offers plenty of benefits to businesses.

First, it allows you to instantly retrieve and optimize data via tailor-made reports. Rather than having to analyze the datasets yourself and then determine how you will convey the information visually, data visualization tools automatically take that information and turn it into your desired chart or graph. This speed of creation also allows you to explore business insights faster, thus increasing the speed of your own decision-making.

Additionally, data automation tools are frequently updated, and many have large online communities that share their own creations and designs. This allows you to stay on top of the game by discovering the latest trends, not only among your own data but also relating to visualization design trends themselves.

Finally, data visualization tools are able to absorb large amounts of data while i

What is a database?

Understanding databases

Database is a term you likely regularly hear, particularly when discussing the buzzword of the 21st Century: data. While there's no denying these two terms are intrinsically linked, you may be surprised how vital databases are to the collection, access, and storage of data — particularly for your own business.

Data might be driving our increasingly digital world, but you'd be forgiven for not knowing a lot about the subject of databases. Thankfully, we've created this in-depth guide to help you learn not just what is a database, but also its evolution, benefits, challenges, examples in everyday life, and so much more.

Database (DB)

1. Database defined

A database is an organized collection of related data, or information, which is stored and accessed electronically within a computer system. This data is usually controlled by a database management system (DBMS). Through the DBMS, users can also store and retrieve large amounts of data. This database management system, along with its data and associated applications, is referred to as a database system — or simply ‘database’ for short.

Most databases use what is known as structured query language (SQL) for writing and querying data. Additionally, the data within these databases are typically modeled — or represented — in the form of rows and columns within tables. Setting out the data in this way allows it to be easily accessed, controlled, revised, updated, managed, and structured.

There are many different types of databases, including relational databases, NoSQL databases, object-oriented databases, cloud databases, and more. We examine all of these in additional detail in Chapter 4.

2. What is SQL?

In the previous chapter, we briefly mentioned SQL (or structured query language), along with the fact that most databases use it.

To elaborate on exactly what SQL is: It is a programming language that is used to communicate with a database. SQL is so widely used, that the American National Standards Institute (ANSI) deemed it the standard language for relational database management systems.

Relational databases are those we described in the previous chapter which use SQL and present their data in rows, columns, and tables.

Just some of the most well-known relational database management systems that use SQL include:

Oracle

Sybase

Microsoft SQL Server

Access

Ingres

The SQL language communicates with databases in a manner that allows users to perform vital tasks such as updating or retrieving data. Standard SQL commands, for example, include Select, Insert, Update, Delete, Create, and Drop. Together, these six basic commands allow a user to carry out practically anything they may need to do within a database.

It’s also important to point out that although the majority of database systems use SQL, most of them also have their own additional built-in extensions, which are unique to their system alone.

3. Evolution of database

The database isn’t a new concept; however, it is one that has continued to evolve over time to become the intelligent, big data-housing system that we know of today.

Data modeling and databases actually date back to the 1960s, and their evolution can be discussed in five key phases.

In the very beginning, database management systems were developed to adhere to three different models: the hierarchical model, network model, and inverted file model.

This took place from the 1960s, however in the 1990s, a new model of DBMS was born: the object-oriented model, also known as object databases.

The second phase of the database’s evolution came in the form of the relational model, which introduced all of the SQL products, along with a few non-SQL products, during the early 1990s. In 2008, this database model began to experience some decline.

Online Analytical Processing (OLAP) and specialized DBMSs were also introduced around 1990, both of which are still quite popular to this day.

The fourth phase, known as the graph database phase, began in 1999 with The Semantic Web stack from the Worldwide Web Consortium. In 2008, this trend continued with the emergence of property graph databases.

Finally, the most recent stage of the evolution of the database began in 2008. Known as the NoSQL phase, it includes databases as we know of them today, including the big data phenomenon.

4. Types of databases

By now, you’ve probably realized that there are numerous different types of databases, each with its own properties and systems. Let’s take a closer look at the various database types below, along with some well-known examples of each.

1. Relational databases

Relational databases are one of the most well-known and widely used types of databases. They get their name from the way they store their data, which is in many related tables. These tables then feature the information in rows and columns. Because of this, relational databases are very reliable, and they work well with structured data. This makes them unsuitable for companies that work with a lot of unstructured or semi-structured data, however.

Relational databases also utilize SQL (structured query language, as discussed earlier in Chapter 2) to read, create, update, and delete data. The program that allows users to create, update, and manage a relational database is known as a relational database management system (RDBMS).

This type of database is also compliant with what’s known as ACID, standing for Atomicity, Consistency, Isolation, and Durability. When these four properties are present, it assures a database transaction is reliable and accurate, despite any other errors which may occur.

Examples of well-known relational databases include:

Microsoft SQL Server

Oracle Database

MySQL

PostgreSQL

IBM Db2

2. NoSQL databases

You’ve learned about databases that use SQL, but what about NoSQL? NoSQL databases are simply those that don’t use structured query language as their central data access language. They are also referred to as non-relational databases because, unlike relational databases, the data in a NoSQL database doesn’t have to obey a predefined structure or model. Because of this, they are perfect for businesses or users looking to store and oversee both unstructured and semi-structured data.

Another benefit of a NoSQL database is the fact that developers are able to make a change to the database “on the fly” — or as it is carrying out other tasks — without affecting the programs that are using that database.

Examples of NoSQL databases include:

Apache Cassandra

MongoDB

CouchDB

CouchBase

3. Cloud databases

As the name suggests, a cloud database is one that has been designed to run “in the cloud” — or in other words, in servers that are accessed over the Internet. Because they are offered in this software as a service (SaaS) distribution model, these databases are often low maintenance. Additional benefits include flexibility, scalability, and high availability.

Some common examples of cloud databases that you may have heard of include:

Microsoft Azure SQL Database

Amazon Relational Database Service

Oracle Autonomous Database

4. Columnar databases

Columnar databases store their data in columns rather than rows. Also known as column data stores, this database model is well-suited to processing analytical queries, thus making them common within data warehouses.

When requesting to generate data from a columnar database, you can only retrieve information from the columns you want. Therefore, it basically ignores all of the data that doesn’t apply to your query.

Just some examples of columnar databases include:

Google BigQuery

Cassandra

HBase

MariaDB

Azure SQL Data Warehouse

5. Wide column databases

Wide column databases, also referred to as wide column stores, are perfect for supporting real-time big data applications. Because they store their data in column groups rather than rows and columns, they are both highly scalable and can handle an incredibly large amount of data.

Three wide column databases you may have heard of include:

BigTable

Apache Cassandra

Scylla

6. Object-oriented databases

With an object-oriented database, all of its attributes are tied together as an object. These databases are based on object-oriented programming and managed by object-oriented database management systems (OODBMS). As a result, an object-oriented database responds to object-oriented programming languages, such as Java and C++. Furthermore, these databases conform to ACID (Atomicity, Consistency, Isolation, Durability standards), just like relational databases.

Examples of object-oriented databases are:

Wakanda

ObjectStore

7. Key-value databases

Key-value databases are a type of NoSQL database. Also referred to as a key-value store, these databases save data as a group of key-value pairs containing two data items each. Due to their high scalability and ability to cope with high volumes of traffic, key-value databases are optimal for tasks such as managing sessions of web applications, online shopping carts, or user sessions for giant multi-player online games.

Two key-value databases include:

Amazon DynamoDB

Redis

8. Hierarchical databases

Known as a parent-child model, hierarchical databases organize data in a family tree-like model, with one object branching out to multiple objects underneath it. It was originally developed by IBM during the early 1960s, so is therefore one of the earliest database models around.

The family tree-like structure that hierarchical databases use is inflexible, so child records cannot have more than one parent record (the data from which it stems). Even to this day, however, this type of database is commonly used to support high-performance and high-availability applications.

Examples include:

IBM Information Management System (IMS)

Windows Registry

9. Document databases

Document databases are also commonly referred to as document stores or document-oriented databases. They differ from the other databases we’ve talked about so far in the sense that they use JSON-like documents to model data rather than rows and columns. JSON documents comprise of fields, which are name-value pairs. The fields can be in any order and be nested or arranged in arrays (a data structure consisting of a collection of elements).

Document databases were created to store and manage document-oriented information, which is simply another term to describe semi-structured data. Because this database model is straightforward and easy to scale, it’s a great solution for mobile apps that require regular and fast updates or improvements.

Three well-known examples of document databases include:

MongoDB

Amazon DocumentDB

Apache CouchDB

10. Graph databases

Graph databases are another example of a NoSQL database; however, this model is also based on graph theory. Graph theory is the study of relationships through graphs, or more specifically, a method for quantifying and simplifying the numerous moving parts of dynamic systems.

Therefore, graph databases are commonly used to analyze the relationships between varied data points, such as in fraud prevention or for mining data about customers from a company’s social media following.

In order to identify and work with the connections between data points, a user must utilize Graph-Oriented Database Management Systems (DBMS) software.

Some graph databases are:

Datastax Enterprise Graph

Neo4J

11. Time series databases

As the name implies, a time-series database deals with time series or time-stamped data. This might include things such as network data, sensor data, and application performance monitoring data, for example.

Just some common types of time series databases include:

Druid

eXtremeDB

InfluxDB

5. Database examples

As you learned in the previous chapter, there are numerous different types of databases. By now, you may be wondering how databases are used in your day-to-day life. From gaming to the products you buy online, databases are extremely versatile. Here are 6 examples to show you how.

1. On-demand online video streaming

When using an on-demand video streaming platform, you may have noticed how it keeps track of your viewing preferences. Platforms such as Netflix, for example, match these preferences with the TV shows or movies they feature on your home screen.

Video streaming companies actually use databases to do this, as they have a staggering amount of data to organize and analyze at any given time. Doing so allows them to provide more optimal watching recommendations every time you log in to their service.

2. Personal Cloud Storage

Personal cloud storage systems you might use include Dropbox, Google Drive, Microsoft OneDrive, and iCloud. Each of these use complex data models and powerful data warehouses to guarantee your data is stored securely and is available to you any time you need to access it — day or night.

3. Social media

Social media platforms collect and store an extraordinary amount of user information. They also need this data to be cross-referenced, so they can accurately recommend friends, businesses, products, and topics to each user, thus requiring highly reliable and capable database software.

Facebook, for example, runs MySQL in its data centers and has been doing so since its creation. Other social media platforms utilize NoSQL databases, proving that there isn’t a single solution that fits all.

5. eCommerce

Without the use of databases, eCommerce stores simply wouldn’t be able to run. This is because databases are responsible for tasks such as organizing products, pricing, customer information, and purchasing history.

An online store can use their data to assist them with a wide range of things, such as recommending specific products to individual users based on their past purchases, for example.

As a result, a highly secure database is also important to have, particularly one with PCI Compliance certification.

6. Healthcare

Healthcare, including hospitals and doctor’s offices, is another major industry that relies heavily on database usage. They must store vast amounts of patient data and be able to access it both securely and easily. As you can imagine, these databases contain complex data structures to not only handle these enormous amounts of data but also to comply with HIPAA (Health Insurance Portability and Accountability Act of 1996) standards for sensitive data management.

Leading healthcare website Healthcare.gov uses NoSQL databases to manage their health insurance information, for example.

6. What is database software?

We’ve mentioned the term database software multiple times now, so let’s take a closer look at what database software is, as well as how it is used.

Database software allows users to create, change, and manage database files and records. Without it, tasks such as data entry, data editing, updating, and reporting would be incredibly difficult. Database software is also responsible for things such as storage, backup, multi-access control, and security of data.

This last point is particularly important, particularly as volumes of data continue to increase and play a major role in our world. Data theft has become more frequent over time and is an increasingly important issue that organizations must protect against.

Additionally, database software can be praised for making the process of data management much easier. It does this by allowing users to store data in a structured form then access it. Usually, the database software has a graphical interface from which users can create and manage the data. In more advanced situations, users may even be able to construct their own databases through the assistance of database software.

7. What is a database management system (DBMS)?

A database management system — or DBSM as it’s shortened to — is quite similar to database software, as explored in Chapter 6. Its primary purpose, however, is to function as an interface between the end-user and the database. To do this, the DBSM must manage the data, database engine, and database schema all at once in order to enable the organization and management of data.

The main features and capabilities of a DBMS include:

A user-accessible catalog describing metadata

A DBMS library management system

Data abstraction and independence

Data security

Recording and reviewing activity

Concurrency and transaction support

Support for authorization of access

The ability to access support from remote locations

DBMS data recovery in the event of damage or loss

Applying restrictions to ensure data follows specific rules

A database management system operates through the use of system commands. First, it obtains instructions from a database administrator in the management system. Then, when it is instructed accordingly, it either retrieves data, modifies data, or loads existing data from the system.

8. What is a MySQL database?

MySQL database is another term you will encounter when dealing with data. Simply put, a MySQL database is an open-source relational database management system, or RDBMS.

It gained its name from the word ‘My’ (the name of the co-founder’s daughter) as well as SQL (structured query language) which is used to generate, change, and extract data from relational databases. As a type of relational database, it organizes data into data tables, where the relationship between data types helps to structure the data.

MySQL also works with an operating system to create a relational database in a computer's storage system. Additionally, it makes it possible to manage users, provides network access, and helps with testing database integrity, along with data backup.

Most often, MySQL is used with other programs to execute applications that need relational database capability.

MySQL is used by many popular websites, including Facebook, Flickr, MediaWiki, Twitter, and YouTube, for example.

9. Key factors that influence database performance

There are five key factors that influence database performance. These include workload, throughput, resources, optimization, and contention. Let’s take a closer look at each one below.

Workload refers to the demands of the database. This might include processes such as online transactions, batch tasks, ad hoc queries, data warehousing analysis, utilities, and system commands — all of which can be happening at any time. Sometimes, this workload might be predictable, such as a lighter workload after employees go home, or a heavier workload at the end of the month when payroll is processed. Usually, however, the workload can fluctuate drastically — even on a minute-by-minute basis.

Throughput describes the capacity and ability of the hardware and software to process data. It is made up of several elements, including I/O speed, CPU speed, parallel capacities of the machine, the core of the DBMS, and the competence of the operating system and software.

The resources of the system refer to the hardware and software tools at the database’s disposal. These resources include things such as database kernel, disk space, memory, cache controllers, and microcode, for example.

Optimization is another key factor that influences database performance. All types of database systems can be optimized, and many can also perform their own query optim

What is data engineering and what does a data engineer do?

Introduction to data engineering

Data engineering is often referred to as the close cousin of data science. Yet, many people are left wondering: What exactly is data engineering, and what does a data engineer do?

The truth is data engineering is a vital part of our data-driven world, and without it, organizations would find it nearly impossible to succeed. This is because the field makes complex datasets usable, thus allowing data scientists, analysts, and other consumers of data to work their magic.

In this helpful guide, you'll learn what data engineering is, and what a data engineer does including the skills a data engineer must master to succeed within their position.

Data engineering 101

1. What is data engineering?

2. Why is data engineering important?

3. What does a data engineer do?

4. What skills does a data engineer need?

5. How do data engineers differ from data scientists?

Data engineers help your business make better decisions with data

1. What is data engineering?

Companies both large and small have vast amounts of data to sort through to reveal important answers about their business. This is where data engineering comes into play. Data engineering is the practice of designing and building systems for collecting, storing, processing, and analyzing large amounts of raw data to help data scientists and other analysts understand the current state of their business, and make data-informed decisions.

Data engineering is a growing field, and there is an increasing demand for qualified data engineers which can be attributed to five key reasons:

Data is growing faster than ever before. The amount of data continues to grow exponentially each year. In fact, the majority (90%) of the data available today was only created in the last two years.

Data is considered more valuable to companies. It is now being used across many areas of business—including sales, marketing, and finance—and in ways that allow businesses to operate more innovatively and effectively.

The technologies being used for data are becoming more complex. Because data is being created across many different systems—such as relational databases, Hadoop, and NoSQL—companies require specialists to be able to work across these more complicated technologies.

Businesses are discovering more ways to use data to their advantage. Data can help them to understand the current state of their business, predict the future, learn more about their customers, reduce risks, and create new products. Data engineering is the key player in all of these scenarios.

Growth and demand continue to rise for data engineers, thus creating a snowball effect. As data becomes more complex, this role becomes even more vital. Additionally, as demands for data increase, data engineers will be more critical than ever before.

2. Why is data engineering important?

Companies have produced massive volumes of new types of data, including more complicated data. Although companies know that data scientists are needed to make sense of the information, many companies forget that data engineers are necessary to organize and manage this data.

Problems occur when data scientists are forced to do the job of data engineers. When this happens, they often fail to optimize the data pipelines used for projects. Data engineers are important because their duties allow data scientists to do their work more effectively.

3. What does a data engineer do?

A data engineer’s main responsibility is to retrieve, store, manage and transform raw data into an easy-to-analyze format for data scientists and other business analysts to interpret, and effectively use.

As a result, a data engineer will work across multiple departments within an organization, as they need to understand what the business is hoping to gain from these large datasets. In other words, they must first understand a company’s or client’s goals in relation to that data. This, in turn, allows them to build effective processes for uncovering and using the data.

Along with optimizing data retrieval, a data engineer also develops dashboards, reports, and other visual assets for a company’s stakeholders. Additionally, they may be tasked with communicating data trends. All in all, however, a data engineer’s role and responsibilities will differ slightly across large or small organizations. A big company may have several data analysts or scientists onboard to assist with understanding data, while smaller companies might rely on a single data engineer to fulfill all responsibilities.

To fulfill these responsibilities, a data engineer must undertake a number of tasks. These include:

Acquisition: Sourcing the data from various systems

Cleansing: Identifying and fixing errors

Conversion: Changing data from one format to another

Disambiguation: Interpreting data that has various meanings

De-duplication: Eliminating duplicate copies of data

Find a data engineer for hire

4. What skills does a data engineer need?

There are many specialized skills that a data engineer must possess to fulfill their role. Let’s take a look at each of these below.

General skills

Data engineers need to have general skills that allow them to create software solutions that involve data. There are between ten and thirty different tools data engineers may need to use, which vary between industries.

However, some skills are necessary regardless of the industry. For example, data engineers must always be proficient in SQL. Other necessary skills include:

Background with software engineering

Knowledge of distributed systems like engineer and software architect skills

Experience with open frameworks

Proficiency using SQL

Programming, especially with Python (although other programming languages may be used by different industries)

Use of Pandas (a Python library used to clean and manipulate data)

Use of cloud platforms

Statistical analysis skills

Knowledge of data modeling

Ability to use visualization and dashboards

While there may be different needs between industries, the above skills are a good starting point for data engineers.

Technical skills

In addition to the skills listed in the previous section, data engineers need a number of technical skills. The following are different types of technologies used by data engineers:

Use of Oracle, IBM DB2, and Microsoft SQL Server to create relational databases for HR, financial planning, and CRM

Use of Vertica, Teradata, Amazon Redshift, and Sybase IQ to create analytical relational databases for data warehouses and data marts

Use of MongoDB to create JSON databases for web, social, and mobile

Use of Redis and Memcached to create key-value systems for web, social, and mobile

Use of Apache HBase and Apache Cassandra to create columnar databases for IoT and machine data

Use of Hadoop Distributed File System (HDFS) to create file systems for data storage

Use of Azure Blob Store and Amazon S3 to create object stores for data storage

Use of Microsoft Excel to create spreadsheets for desktop data analysis

The exact applications used will vary between industries and companies, however.

5. How do data engineers differ from data scientists?

For some, the roles of data scientist and data engineer may seem confusing, especially since both fields involve gaining valuable insights from data. It’s important to understand the distinct differences between these roles, as a data engineer and data scientist differ in many ways.

These differences are highlighted in their points of focus, responsibilities, languages, tools, software, and educational background. Let’s examine each below.

Points of focus

A data engineer’s main focus is building infrastructure and architecture to be used for generating data. On the other hand, a data scientist’s primary focus is performing statistical analysis and using advanced mathematics on the data that is generated.

You see, although a data scientist simply interacts with the data infrastructure and architecture built and maintained by a data engineer; they are not in charge of actually creating and managing it. Instead, a data scientist can be viewed as an internal client who must perform high-level research to identify trends and relations among data. They do this using a variety of machines and methods.

Therefore, we can say that data engineers work to support data scientists and data analysts, as they provide the infrastructure and tools that are used to deliver solutions to complex business problems. As a result, data scientists heavily depend on data engineers.

Responsibilities

We can also look at the key differences between the responsibilities of data engineering and data science. We discussed the responsibilities of data engineers in Chapter 2. In a nutshell, data engineers are responsible for managing, enhancing, overseeing, and monitoring the retrieval, storage, and delivery of data throughout the business. Often, they will be dealing with raw data that contains inconsistencies. It’s therefore also part of their job to recommend and implement ways to improve the reliability, efficiency, and quality of this data so that it is “usable” to data scientists.

Alternatively, a data scientist’s responsibilities include gaining access to the data that has been processed by the data engineering team. Usually, this data has undergone a round of cleaning and manipulation, and it is then further analyzed by data scientists. They will perform industry research and establish a business’s questions to determine what they need to achieve with this data. Data scientists will also leverage large amounts of data from internal and external sources to answer these questions and may even further examine this data to uncover patterns.

When we examine the key differences across the responsibilities of a data engineer and a data scientist, it boils down to this: A data scientist needs to know about statistics, mathematics, and machine learning to build predictive models, whereas a data engineer will work with database systems, data API and tools for ETL purposes. Additionally, they will be involved in setting up data warehouse solutions and data modeling.

Languages, tools, and software

Data engineers and data scientists also differ greatly in the languages, tools, and software they use to perform their responsibilities.

Data scientists mostly work with advanced analysis tools including R, SPSS, Hadoop, and advanced statistical modeling software, whereas data engineers are focused on the products which support those tools. Therefore, a data engineer’s armory includes Spark, Hadoop, MongoDB, and other data organization services, as covered in Chapter 4.

The main difference here is that the tools and software regularly used by data scientists reflect the role’s emphasis on storytelling and data visualization. For data engineers, however, their toolkit is reflective of their responsibility to design and build data systems and architecture.

There are also multiple differences between the languages used by data engineers and data scientists. Data scientists frequently use languages such as SPSS, R, Python, SAS, Stata, and Julia to perform their work. Data engineers, on the other hand, utilize some different languages, including SQL and Scala.

Educational background

While data engineers and data scientists both have educational backgrounds in computer sciences, each has specific fields of study that are unique to them.

Data scientists have often studied econometrics, mathematics, statistics, and operations research, for example. This means they have more business expertise than data engineers, whose background often lies in engineering or computer engineering.

Data engineers help your business make better decisions with data

After reading this guide, you now know exactly what is data engineering, along with what a data engineer does. As you’ve learned, there’s a lot more to data engineering than meets the eye. Once lumped under the umbrella of data science, the role of a data engineer is now a distinct field that is rapidly growing in importance each year.

As the volume of data continues to significantly increase, it comes as no surprise that data engineering is only predicted to rise in significance for businesses small and large. After all, data engineers have the vital role of managing, enhancing, overseeing, and monitoring the retrieval, storage, and delivery of data throughout the business. In doing so, they make vital data more usable for a number of key stakeholders. Without data engineering, businesses simply wouldn’t be able to utilize complex data in the ways they do today.