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Data is gathered and accessed in a database and analyze and consumed in a data warehouse. To differentiate between these two terms, we need to understand how the data is accessed, stored, consumed, and retrieved from them.

Let’s dive into the top differences between a database and a data warehouse, what are databases and data warehouses, how they operated, their design technique (normalization), the use cases, examples, advantages, and disadvantages.

Database vs. Data Warehouse?

The database is a structured place to store data in a schema form to manage the Online Transactional Processing (OLTP) operations with robust and matured data security levels. In simple terms, it is a place where all data is gathered, stored, changed, and recovered by anyone.

A data warehouse is an integrated, subject-oriented collection of data extracted from different databases. Data is stored here in a denormalized form to be used for Online Analytical Processing (OLAP) to aggregate queries and analytics.

Operations

The database is used to manage the daily transactions of an organization. The OLTP operation also includes inserting, updating, and removing data from a database.

A data warehousing solution’s main purpose is reporting and data analysis. It is used for OLAP and storing cleaned data.

Designs

Entity-relationship (ER) model-based design is used in databases, it can be changed accordingly. Data is stored in a normalized form to facilitate simple queries. An ER model-based design usually requires a smaller space. All databases require regular data backups to function.

In a data warehouse, snowflake design is used which can be changed according to the reporting subject. It usually requires ample space for storage. Data is stored in the denormalization form to improve query performance (like star schema). Contrary to databases, backups are rare as it functions for business intelligence purposes only. 

Use Cases

Usually, a database is used to manage everyday operations of a business like collecting sales data, resolving standard and straightforward customer queries, and managing online orders and transactions.

On the other hand, a data warehouse is used for storing data for resolving large and complex queries like aggregation. It provides relevant recommendations based on historical data for business intelligence.

Examples

In online banking, a database is used to check account balance and managing the fund balances. Many e-commerce platforms use it to create orders and add products to the shopping cart. For a call-center, a database serves to view or update the customer's details.

In the finance department, a data warehouse is used for making budgets and developing financial strategies. For the sales department, it is used for sales analysis and forecasting. In the marketing department, data warehouses aid in market analysis, promotion and sales forecasting, customer analysis, and target market segmentation.

 Advantages

In a database, you can easily manage daily transactions and operations. With mature and robust data security, multiple users can use OLTP at a single time. Can solve Frequent queries and updates with faster response time compared to OLAP.

The data warehouse creates a single platform for all business analytical needs. It complies with regulations to protect sensitive information while enhancing data quality and business intelligence to drive informed decisions for the organization.

Disadvantages

In a database, even a small hardware failure could affect online transactions and cause delays. With multiple user access, uncommon situations of data change can occur. OLTP compatibility requires costly system designs and maintenance. Also, the cost of storing and maintaining data is high compared to OLAP.

A data warehouse requires ample space for data storage than OLTP. Additionally, IT dependence on implementing and maintaining an OLAP system may prove to be costly for small businesses.

The world of data has been growing exponentially, and the data management industry is totally changed from what it was a few years ago. Around 90% of the current data has been generated in the last couple of years only. According to a report by Domo, our continuous data output is nearly 2.5 quintillion bytes in a day, which means there’s massive data generated every minute. With technological transformation, data has become a critical factor in business success. Above all, processing data in the right way has become a pivotal solution for many businesses around the globe.

If we look back, terms like a data lake, ETL (Extract, Transform, Load), or warehousing would have been baffling for most people, or merely some buzzwords.

Today, data management technology is growing at a fast pace and providing ample opportunities to organizations. Organizations these days are full of raw data that needs filtering. Systematically arranging the data to get actionable insights for decision-makers is a real challenge. Thus, meaningful data accelerates decision-making, and using ETL tools for data management can be helpful.

The evolution of Extract Transform Load (ETL)

Data warehouses and ETL tools were created to get actionable insights from all your business data. There are currently several ETL tools in the market that have expanded functionality for data cleansing, data profiling, big data processing, master data management, data governance, and Enterprise Application Integration (EAI). With the availability of data in the warehouse or Online analytical process (OLAP) cube, a Business Intelligence (BI) software is used to visualize and analyze them. This software helps with reporting, data discovery, mining, and boarding.

The complete process

The process of extracting and organizing raw data, transforming to make it understandable, and loading it into a database or a data warehouse to easily access and analyze it, is known as an ETL process. In short, it’s an essential component in the data ecosystem of any contemporary business.

Since data coming from various sources has a distinct structure, every dataset needs to be transformed differently before using it for business intelligence and analytics. For example, if you organize data from source systems like Google Analytics and Amazon Redshift, these two sources should be treated separately with the whole ETL process.

Implementing the ETL process in the data warehouse

The ETL process includes three steps:

1.    Extract

This step comprises data extraction from the source system into the staging area. Any transformations can be done in the staging area without degrading the performance of the source system. Also, if you copy any corrupted data directly from the source into the database of the data warehouse, restoring could be a challenge. Users can validate extracted data in the staging area before moving it into the data warehouse.

The data warehouses should merge systems with hardware, DBMS, OS, and communication protocols. Sources include legacy apps like custom applications, mainframes, POC devices like call switches, ATM, text files, ERP, spreadsheets, data from partners, and vendors. As a result, you need a logical data map before extracting data and loading it physically. The data map represents the connection between sources and target data.

Conclusion

Every business around the world, whether small, mid-sized, or large, has an extensive amount of data. However, this data is nothing without using a robust process to gather it. Implementing ETL in data warehousing provides a full context of your business for the decision-makers. The process is flexible and agile that allows you to swiftly load data, transform it into meaningful information, and use it to conduct business analysis.

Freight railroads, regardless of size, are always working towards the goal of zero accidents. Through consistent, effective operational testing, rail employees are held accountable to safety procedures that comply with Federal Railroad Administration (FRA) safety rules and regulations.

But while the road to safety may be paved with good intentions, it can also be complicated by a lack of visibility into how safety standards are enforced enterprise-wide, or how effective your operational testing program is.

What’s causing this?

The state of ops testing

Operational testing (also known as ops-testing) is nothing new for Class I, II, and short line and regional railroads in North America. Mandated by the FRA, railroads must ensure that their employees are complying with safety procedures that, at the end of the day, keep them safe, and create a safe work environment.

But over time, accidents and incidents occur. They pile up—to the point that 39% of accidents and incidents from 2013-2017 were due to human error. There may be a higher rate of accidents in one geographic location or due to conductor errors, but how do you know? Since the operational tests are conducted and captured on paper spreadsheets, the task of shifting through file after file and try to make sense of all the data without a tool to facilitate insights is daunting. It’s hard to tell if testing officers are meeting test quotas, and you may suspect that testing is more reactive to upcoming audits or following accidents, as opposed to empowering your employees to predict and stay prepared for the next inspection.

Is there an alternative approach to operational testing that ensures that good intentions align with measurable, trackable results?

Adding intelligence to ops testing

With intelligent operational testing, leveraging BI and data analytics, testing optimization, and artificial intelligence (AI) and machine learning (ML), freight railroads can identify and respond to trends to ensure more effective, efficient, and compliant operational testing.

Intelligence takes your ops testing program farther than a series of checklists and to-do’s conducted on a—hopefully—regularly basis. For example, business intelligence (BI) and analytics dashboards help you monitor safety parameters in a way that is hard to accomplish manually, scanning through paper sheets containing rows and rows of data. There’s a shift from responsive, time-consuming testing to repeatable, trigger-based test plan creation.

No matter if you’re a short line or Class I railroad, there are several ways that you can add intelligence to your operational testing

Set up a strong data management process. Gather and store measurable and measured ops testing data in a cloud environment for cross-department visibility, quick accessibility, and future data analysis.

Conduct trend analysis: Once you’ve migrated your data to the cloud, you can leverage data analysis to identify trends and patterns, and then respond to trends in your ops testing. You’re using your data to make smart business decisions, not just safety decisions. Where are you allocating your resources, be they money or manpower? You’ll be able to see where test failures are periodically occurring by location, role, time of day, weather conditions or employee. With trend analysis, you can harness the power of analytics to optimize your inspection allocations, increasing inspection frequency where the risks are highest.

4. Respond to AI/ML recommendations: This takes your intelligence a step farther. AI and ML capabilities can enable reporting for senior leadership, making the story of your safety clearer. You can also get recommendations so you can focus on departments or territories and see where compliance breaches are happening more often historically, and then predict where and when safety incidents are likely to occur, based on causal factor correlations as well as historical trends.

Where intelligent operational field testing can take you

Bringing together what-if scenarios that help you optimize your testing and AI/ML algorithms to provide intelligent recommendations, railroads can move from minimum to intelligent ops testing, resulting in comprehensive test plans, better test schedules, and enhanced employee and department tracking. Applying this approach to your operational field testing leads to higher enforcement and efficiency (thus, cost savings) of ops testing and safety procedures, reduced penalties, and increased safety.

The operational field testing journey with Kansas City Southern

Kansas City Southern (KCS) is one of today’s innovative, visionary railroads. Recognizing the value of applying leading-edge, innovative technologies such as cloud, big data analytics, and machine learning, they partnered with us to leverage these technologies to drive improved efficiency of operational testing.

A growing number of organizations are investing in advanced technologies to empower their business intelligence programs. There’s an industry-wide shift in the way we understand and utilize data today and data visualization tools have a lot to do with this shift. Advanced data visualization tools offer a suite of sophisticated features along with the data analytics capabilities. Power BI is among the most powerful tools available in the market space. It is enabling organizations to make time-sensitive decisions by providing actionable insights from enterprise data within a short time.

With Power BI, you can generate interactive visuals in reports and dashboards to understand relationships between parameters, capture the latest trends, and track projects or campaigns. One of the best practices to follow while using Power BI is to undertake some level of performance tuning for your reports. It could be for a variety of reasons like Power BI got updated with the latest features and you want to assess/enhance the performance, or you encountered performance issues and want to identify the bottlenecks.

To optimize the performance of Power BI reports, you can explore some of the inbuilt features and take advantage of a few open-source tools available in the market. The table below highlights five tools to assess the performance of your Power BI reports and provides ratings for their features as well.

Performance Analyzer in Power BI Desktop

It evaluates and displays the duration required for updating or refreshing the visuals, helping the user to identify which visual or element is impacting the performance of the report. Some of the features of Performance Analyzer are:

Capability to record the user actions and log time for each activity which allows users to drill down and find the bottleneck.

Collects and display real-time information related to performance.

Captures the duration time in milliseconds for each visual on the page which helps to tune the performance of the report.

Each visual’s log information comprises of the duration to complete the following types of tasks – DAX query, visual display, and others (waiting time for pending operations on a page).

Allows saving the performance analyzer list generated by the tool in the JSON format using the ‘export’ option. It helps to compare operations after completing multiple required actions.

Conclusion When professionals are generating Power BI reports, if there’s a bottleneck that leads to slow performance, it can seem challenging to drill down and find the exact area that needs attention. Designing reports for optimal performance requires performance tuning and it’s not always the simplest part of using Power BI. It is so because multiple factors can affect the performance and they need to be investigated. The tools covered in this blog post can enable users to identify the source of performance issues at a granular level.

It’s not easy to train machines on how humans communicate. In recent years, numerous technological innovations have enabled computers to recognize language the way we humans do.

This blog will introduce Natural Language Processing and some of its essential aspects.

What is Natural Language Processing

Natural Language Processing (NLP) is a branch of artificial intelligence dealing with the interaction between humans and computers using a natural language. The ultimate aim of NLP is to read, understand, and decode human words in a valuable manner. Most of the NLP techniques depend on machine learning to obtain meaning from human languages.

A usual interaction between machines and humans using Natural Language Processing could go as follows:

Humans talk to the computer

The computer captures the audio

There is an audio to text conversion

Text data is processed

Data is converted to audio

The computer plays the audio file and responds to humans

The use of Natural Language Processing

These are the typical applications where NLP is playing a driving force:

NLP is used in language translate applications such as Google Translate

It has excellent use in word processors like Microsoft Word and web apps like Grammarly that uses NLP to check the grammatical accuracy of texts

Interactive Voice Response (IVR) apps used in the call center to answer to specific users’ queries

Personal assistant apps such as Alexa, Siri, OK Google, and Cortana

How does Natural Language Processing work?

Natural Language Processing requires applying algorithms to recognize and bring out the rules of natural language so that the raw language data is transformed into a machine-understandable form. When we provide text to the computer, it uses algorithms to understand the meaning related to every sentence and gather the essential data from them. However, there are times when these computers fail to extract the exact meaning of a sentence, which could lead to uncertain results.

For example, one of the common errors is with the translation of the word “online” from English to Russian. In the English language, online means “connected to networks,” but its Russian translation has a synonym that means “interactive.”

Another example is a bot-based English sentence-restructuring tool that translates the sentence in such a way that it can change the whole meaning.

Here is a common English proverb, which we need to reframe:

The spirit is willing, but the flesh is weak

Here is the sentence of how that tool rewords it

The soul is prepared; however, the tissue is powerless.

An advanced data warehouse, also known as an enterprise data warehouse, serves as a data hub for business intelligence. It is a support system that stores data across the organization processes it, and enables it to be utilized for various business purposes, including reporting, business analysis, and dashboards. A data warehouse system stores structured data from multiple sources such as Online Transaction Processing (OLTP), Customer Relationship Management (CRM), and Enterprise Resource Planning (ERP).