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The augmented analytics market is growing at a fast pace of 25 percent every year which is making some significant implications for the HR segment. With the rapid rise in data volumes globally, companies are determined to extract deep insights about their workforce and generate value. But the constant dependency on data scientists is still a big challenge for most of them. In particular, small and medium-sized companies do not have the resources and skill set to extract the full potential of data. This condition is true of non-technical functions as well, say HR. Due to the absence of data science talents, HR may struggle to derive meaningful and actionable insights. This may further result in fewer benefits from huge investments in analytics model development. This is where augmented analytics comes into the picture. The augmented analytics democratizes data-to-insight conversion enabling virtual access to insights in a comprehensible format to any stakeholder.

Augmented Analytics in HR

It can be described as a branch of data science aiming to automate the insight generation process by using cognitive technologies. This enables machines to view and represent data from a human perspective. Components of Augmented Analytics in HR: Machine Learning, Natural Language Processing, and Insight Automation. Machine Learning enables technology systems intuitively learn, eliminating the need for intervention of human coders. The system can automatically adapt to different circumstances independent of rule-based programming. ML-powered augmented HR analytics provides better and right insights with every data processing cycle. Natural Language Processing is quite relevant to HR as it does not require HR professionals to have years of data science experience rather the augmented HR analytics interface can deliver insights in a human-readable format. Insight automation can take over the tiring work of data scientists who spend 19 percent of their time on collecting data and 60 percent on cleaning and organizing it as per conventional analytics model. Instead, data science professionals can focus on developing more effective training sets and refining analytics algorithms. HR only needs to input correct data into the interface in order to receive the most relevant insights. These three components make HR analytics quite easier to use.

Use Cases

Augmented analytics is not confined to one HR function or area rather. much like the internet, it has the potential to transform processes across the entire enterprise. For example:

Aligning Hiring Efficiency to Employee Quality

In the current times where the labor market is going through intense competition, HR risks compromising quality due to uneven focus on quantity. There exists a great rush for deadlines, time-to-hire goals and recruitment campaigns to be kept under budget. This results in undermine the quality of hire. The HR augmented analytics enables the enterprise to feed recruitment data into software and assess where it stands in the quality bandwagon.

Controlling Voluntary Attrition

For enterprises, attrition is a complex issue. In some cases, voluntary and voluntary attrition is not at all regrettable. Therefore, augmented analytics enables the enterprises to deep dive into all such characteristics, sifting through employee tenure information and highlighting the cause and nature of attrition. The consequent attrition insights can help enterprise refine the employee management mechanism for optimal attrition rates, targeted towards most high-performing and ROI-friendly employees.

Employee Engagement  Benefits AI with Analytics

Several companies are eager to embrace AI technology but are not sure of the right utilization. Amid these augmented analytics takes a solution-oriented approach for adopting AI and identifying a measurable HR challenge while using data to solve it. Consequently, the company can obtain tangible returns from its investment in AI.

Reduction in Time

Augmented analytics has a clear take on manually driven analytics systems. As data scientists can take a long time to collect and cleanse the data and building analytics models, augmented HR analytics automates the 50 percent of the work significantly increasing insight generation. Notably, this approach is faster than regular analytics. A person need not spend time converting such insights into action points because the predictive capabilities of augmented analytics will indicate a clear course of action.

Cost Reduction Conclusion

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How Big Data Analytics Is Transforming The Casino Industry

As one of the world’s most cutthroat markets, it is no surprise that the casino business has jumped on the big data analytics bandwagon. Casinos may improve their tracking of consumer behaviour, learn more about their patrons’ requirements and preferences, and streamline their operations using big data analytics. Notably, some of the casinos reviewed on SF Gate are utilizing big data analytics to tailor their offering to suit specific customers. The article takes an in-depth look into how casinos utilize big data analytics to streamline their operations. 

Identifying customer preferences and trends

With big data analytics, casinos may better understand their customers’ tastes and behavior by gathering, analyzing, and drawing conclusions from massive volumes of data. Customer profiles, purchase histories, gaming tendencies, loyalty program participation, and other types of information may be collected. Casinos may use this data to understand their patrons better and cater to their needs. For instance, they may be able to tell which games are the most well-liked among people of a certain age or gender. In addition, they could ascertain which campaigns are most successful in generating client engagement or loyalty. Now that they have this information, they may make educated guesses about improving their business for optimum profit.

Promoting responsible gambling practices

There has been a rise in the importance of big data as a tool for responsible gaming. Operators may learn a lot about their customers’ habits by gathering and analyzing a lot of data. The data may help identify problem gamblers before they fall too far into debt. One other way operators may benefit from big data is by gaining a deeper understanding of client preferences and using that knowledge to improve the quality of their offerings. The operators may provide more customized incentives and promotions for each player by, for instance, monitoring their spending habits.

Ensuring customer privacy using big data analytics

There are several measures casinos use to protect their client’s personal information. Encryption is one of the most vital methods. Casinos utilize sophisticated encryption methods to protect all player information, making it almost difficult for unauthorized parties to decrypt player records. To further ensure the privacy of their patrons, casinos often use anonymization methods. Before using data for analytics, it must be cleansed of any identifying characteristics. To round things out, casinos have stringent procedures limiting who may access consumer data and how it can be utilized. To guarantee that the most up-to-date measures always protect the privacy of their customers, casinos examine and revise their policies regularly.

Automated decision making using big data analytics

Implementing fully automated decision-making might drastically alter the future of online gambling. Casinos can now make better, more efficient judgments regarding gameplay thanks to the use of algorithms and AI. The casino’s bottom line and the patrons’ overall satisfaction both stand to benefit from this. By identifying questionable behavior and taking action, automated decision-making might also assist in eliminating fraud and cheating in online casinos. Further, player data might be analyzed using automated decision-making to learn about preferences and design games appropriately. Lastly, automated decision-making might be used in developing new games and enhancing current ones to maintain player interest and satisfaction. As a result of all these positive implications, it’s evident that automated decision-making merits additional exploration for its possible uses in the online gambling sector.

Challenges arising from implementing big data analytics

The massive volume of data is a significant obstacle for casinos attempting to use big data analytics. Customers’ gaming habits, financial transactions, and preferences contribute to the massive amounts of data generated by casinos. Gaining valuable insights into client behavior requires collecting, analyzing, and storing this information which is challenging. Casinos also must safeguard this information against hacking attempts. Validating the accuracy and consistency of the analytics is another obstacle to overcome. Last but not least, casinos should think about how they will put the results of big data analytics to use to boost their business. 

Predictive analysis using big data analytics

Lastly, casinos may use predictive analytics to jump on the competition and maintain long-term profitability by predicting industry trends. Using predictive analytics, casinos can see how varied incentives affect patrons’ propensity to return and spend. It enables businesses to optimize their offerings for maximum profit with little sacrifice to client satisfaction.

How To Check Backlinks In Google Analytics 4 (2023)

This guide is intended to help you understand and make the best out of backlinks analytics. 

You will know how to find backlinks in GA4 so that you can unleash opportunities such as improving your ranking on search engines, building partnerships, protecting your website, and getting more conversions.

This is what we’re going to cover: 

Let’s see how to deal with backlinks in GA4!

When other websites include links that direct to your website, these are referred to as backlinks.

Essentially, backlinks are important because they are considered a key ranking factor by Google and other search engines. The more backlinks you have, the more possibilities you have for ranking. 

Getting backlinks is like having other websites vote in your favor and tell search engines that your site and its content are helpful, reliable, and satisfying to your audience. 

The more you rank, the more chances you have of additional traffic, which ultimately means more opportunities to achieve your goals such as converting prospects into customers. 

Why Should I Check Backlinks in Google Analytics?

Since backlinks are considered votes, the more of them you get (quality ones of course) the more you increase your chances of being at the top of search engine results. Therefore, it is beneficial to know them.

By analyzing your backlinks, you can identify opportunities for growth and gain insight into your competition. Understanding the link profile of your competitors can help you benchmark your performance, identify successful strategies, and uncover areas for improvement.

By closely monitoring your backlinks, you will be better equipped to create effective content, form strategic partnerships, and drive more traffic to your website.

Even though getting backlinks is rewarding, there are some that you don’t want in your reports. 

Apart from those coming from trusted sources, certain types can skew your GA4 stats or even harm your site. As such, some sites have experienced up to 90% bot/fake traffic not infrequently. 

How To See Backlinks in GA4?

🚨 Note: Hold your horses! Before jumping into the report and visiting all the websites that are referred to yours, it’s important to understand the different types of referral traffic, how to analyze them, and how to manage or cut the risks (like malware) that some carry. We’ll show you how to see your backlinks, but make sure to read the next sections. 

In your Google Analytics account, go to Reports→ Acquisition → Traffic acquisition

Scroll down and type the word “referral” in the search bar and press Enter on your keyboard.

You will see your referral traffic aggregated. 

To view all your referrals, change the primary dimension from the Session default channel group to Session source/medium. 

Select the primary dimension: 

Now, select Session source/ medium.

Now you can see all your referrals or backlinks.

To address these problematic backlinks, they should be added to the list of unwanted referrals in GA4. We’ll cover this in the section about how to remove bad backlinks.

Let’s first discuss the types of referrals that can cause issues and that you don’t want as referrals:


Third-party payment processors

Website-managed interactions

Spam referral ⚠️


Self-referrals are traffic that comes from your domain. Most of the time, this is due to analytical implementation issues. The consequences of this are wrong attributions. 

You’ll also have session inaccuracies because each time Google Analytics recognizes a source of traffic as being referral traffic, a new session begins. Therefore, your sessions can be more numerous than they are.

Main self-referral issues: 

a) Untagged landing pages

Ensure that all web pages on your website include the Google Analytics tracking code. This code enables Google Analytics to determine (to attribute is the right term) the source of website traffic, allowing you to identify where your visitors are coming from.

If a user visits your website and lands on a page without the Google Analytics tracking code, the page will be considered as a referrer by Google Analytics, thus a self-referrer.

b) Cross-subdomain sessions

GA4 automatically tracks subdomains and does not require any additional setup. When a referrer website has the same domain as one of your pages, Analytics will not count it as a referral.

c) Incorrect cross-domain tagging

Sometimes a website may lead a user to another domain to register for a course for example. After registering, the user is redirected or simply goes back to the first website. 

Although the user is led to two different websites, there is only one journey and not two. In this case, you do not want the second domain to be a referrer because it is not true. The second domain is not sending new traffic to you.

If you’re facing a similar scenario, read our guide on how to set up cross-domain tracking in GA4. 

Third-party payment processors

Paypal, for example, is a third-party payment processor. 

Oftentimes, a user that purchases on an eCommerce store is led to another domain (like PayPal) to complete the payment. After checking out, the user is redirected back to the eCommerce store. 

Coming back to the site will trigger a new session which will skew your statistics. The new sessions triggered by traffic from third-party payment processors are not reflective of the true user journey. 

You do not want third-party payment processors as referrals. To do so, you must add them to the list of unwanted referrals in GA4.

Spam referral ⚠️

Yes. You’re seeing the warning sign correctly. 

Referrer spam is a black hat marketing tactic that involves a spammer sending fake traffic, also called ghost traffic(traffic coming from bots and not an actual person), to your Google Analytics. 

The objective is to promote their website by luring you into visiting the URLs you see in your referral report. 

By promoting a website via this unethical tactic, one may insert an affiliate link which can result in them getting a commission if you were to make a purchase, for example. Okay, the approach was shady, but you may get something positive out of it.

However, spammers can also use referrer spam to build their backlinks!

Although some of these are sometimes legitimate existing sites, the danger we hinted at is malware and phishing sites.

🚨 Note: You’ll often read that you should be careful about spam referrer links. But, this doesn’t help much with what to do about it. If you’re unsure of the legitimacy of your referrals, you’ll have to visit these sites despite the risk. Just make sure you’re using anti-virus software. 

How Do I Know If My Site Has Bad Backlinks?

It’s a valid concern to wonder which backlinks are legitimate and which are not.

It’s important to note that not all low-quality backlinks are necessarily harmful. It’s not realistic to expect to only have backlinks from highly authoritative sites.

Having backlinks from lower authority domains can also be beneficial, as it increases your online visibility and gets attention from search engines.

So how to differentiate the good ones from the bad ones? 

A quick hint is links that have random alphanumeric characters, these links with weird letters and numbers. Then again, some legit sites sometimes have those from their subdomains. 

🚨 Note: The quickest giveaway, are metrics with extreme values like 100 (or close to it) or plain zeros. New Sessions and Bounce rates with values of 100%, Average session duration with 00:00:00, and 0.00 conversions are usually bad backlinks. 

How Do I Check and Remove Bad Backlinks?

You can check bad backlinks by following the simple steps we detailed earlier in how to see backlinks in GA4. 

To remove bad backlinks, go to Admin → Data Streams (Data Streams are found in the Property column). Then select your domain.

Scroll down to the Google tag section and select Configure tag settings.

Now select List unwanted referrals.

You can start now to add your unwanted domains. Hover on the Configuration box, and select the pencil icon that appears. 

There are 5 ways (or different match types) to add unwanted referrals as you can see: 

With Regex, you can include all your unwanted referrals in one line, but you should know Regex and be certain that the filter works.

Otherwise, keep it simple (sometimes that’s just best), like our example in the picture below. You could add more of them using the same steps. 

Let’s remove PayPal as a referrer:

This is how you remove unwanted referrals and bad backlinks. 

How to Measure Referral Traffic

To measure website traffic, first, use the method we covered earlier to access referrals in your Traffic acquisition report. Don’t forget to add the Session source / medium dimension to view all your referrals. 

From here, you can see how your collaborative strategies with other domain owners are working by looking at the Conversions or the KPIs that mattered to you. 

If you aren’t partnering or working with anyone yet, take note of referrals that are either bringing Users or that have a good Average engagement time per session. 

In the same vein, you can find which content performs best for your referral traffic by adding the Landing page + query string dimensions to see where users from referral sources land.

Use the same method we mentioned above (amount of Users, Average engagement time per session, and conversions). 

A good Average engagement time per session means that the content is engaging and you should do more of it. 

The Bounce rate metric can be available in explorations. You can also use it to know if these pages are meeting their expectations or if they make them run away. 

Use Comparisons to compare your referral traffic with other channels and segments. 

💡 Top Tip: If you have low amounts of referrals, don’t dismiss those that are bringing in few users. That one referral could be a domain with high authority and one with the potential for a fruitful collaboration. This could also be one you should get in contact with to start your link-building efforts.

Improving and Getting More Referral Traffic

There are several things you can do with referral traffic. Here are some examples:

Write guest posts.

Find other referrals similar to those you already have and contact them for possible collaboration.

Maintain your relationship with your partners or collaborators. Keep human contact to preserve what you have already built. A simple thank you email can go a long way in maintaining a healthy and productive relationship.

Contact Influencers who would be interested in your content. This will allow you to reach your audience or expand it.

Analyze the link-building strategy of your competition to understand what there is to improve in your strategy. You can also take note of the sites that send traffic to them (and others that are similar) and contact them.

FAQ How can I see backlinks in Google Analytics 4?

To see backlinks in GA4, go to your Google Analytics account, navigate to Reports → Acquisition → Traffic acquisition. In the search bar, type “referral” and press Enter. This will display your referral traffic, which represents your backlinks.

How do I check and remove bad backlinks in GA4?

To check and remove bad backlinks in GA4, go to Admin → Data Streams → Select your domain → Configure tag settings → Show all → List unwanted referrals. Add the unwanted domains using different match types. You can add the domain you want to remove and save the configuration.

Why should I check backlinks in Google Analytics?

Checking backlinks in Google Analytics allows you to gain insights into your website’s performance, identify growth opportunities, and understand your competition. It helps you analyze the link profile of your website and make informed decisions to improve your backlink strategy.


This guide has provided an overview of backlinks analytics including how to find and check backlinks in Google Analytics 4, how to remove bad backlinks, how to measure your referral traffic, and how to improve your link-building efforts.

It’s worth mentioning that you should verify that your users coming from external websites aren’t being redirected from broken links leading to pages on your website that don’t exist anymore. 

We do cover how to track, find and fix 404 eros in GA4 if you’re interested. 

As always, we hope this post was useful to you.

Guide To Rpa’s Benefits In Analytics In 2023

We had explained RPA extensively in layman’s terms and outlined RPA benefits. As an excellent data aggregator, one of the frequently cited benefits of RPA is improved analytics and big data analytics which has been a priority for executives for the past decade. However, the benefits of RPA to analytics are limited to data federation as it enables multiple databases to function as one.

How does RPA contribute to analytics?

We should consider the analytics funnel above to see where RPA can contribute. Bots have essentially 2 critical functions from a data standpoint:

Create meta data: As they complete tasks, they record their progress and the issues they face for diagnostic purposes. This data can be used for both the client or the RPA provider to identify RPA bugs and improve bot performance.

Enable access to data in legacy systems: Since they overtake tasks that require interfacing with legacy systems, they make previously difficult to access data accessible. This can transform data collection capabilities of enterprises, especially those that depend on legacy systems.

Therefore, bots do not essentially improve analytics capabilities but aid in data collection. Even RPA vendors agree with this, underlining that core benefit of RPA is in data federation: the capability to collect data from many different sources and aggregate it in an easy-to-analyze format.

How does data federation contribute to company performance?

Firstly, data federation should not be a major concern for an SME or startup. However for large companies, it is a major concern as legacy systems historically held large companies back in terms of easy access to data. Now with access to granular data about processes, large companies can reap 2 important benefits:

Process optimization thanks to process mining

Granular data about processes can help identify bottlenecks and inefficiencies, enabling corporations to increase both speed and efficiency of the process. Furthermore, it makes dissemination of best practices easier. Since process flows can easily be visualized with the help of data, process flows in different regions can be compared to find the best processes for the whole company. For example, in one of process mining case studies, Piraeus Bank optimized their loan application processes from 35 minutes to 5 minutes, thanks to process mining technology.

For complex inter-related processes, machine learning techniques could be used to find optimizations that analysts could easily miss. Here are some examples from PwC:

Machine learning might come up with the suggestion that ordering material X from supplier A in the week of Christmas instead of the first week in January will result in a 50% improvement in order fulfilment in January. You could change the RPA robot setting in line with this suggestion to make sure orders to the relevant suppliers are placed during Christmas week, while your staff are on vacation. Apart from its ability to generate simple correlations, machine learning combined with today’s computing power is increasingly capable of identifying unknown relationships within multiple business processes. For instance, it can potentially correlate procumbent processes with sales processes to analyze directly what supply chain management actions need to be taken to improve sales.

Process simulation

Some major decisions like outsourcing, workforce reductions or expansions are made haphazardly, based on urgencies without considering future implications. Such decisions tend to have long-lasting impact because once a process is outsourced or its headcount increased, it is difficult to roll-back such decisions due to risk averseness inherent in humans. Managers, especially those in well performing companies, would like to see how rolling back such changes will not impact operations. Process simulation provides an answer. By simulating how a process flow will be impacted by changes, analysts can show the impact of major changes on the process. RPA systems can provide the necessary data for such simulations.   RPA can have other fundamental benefits for cost management and elimination of manual errors as we outlined in our comprehensive list of RPA benefits.  

For more on RPA

To explore RPA and its use cases in detail, feel free to read our in-depth articles:

If you still have any questions about RPA, feel free to download our in-depth whitepaper on the topic:

And if you believe your business will benefit from an RPA solution, feel free to scroll down our data-driven list of RPA vendors.

And let us guide you through the process:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Analytics: How To Use Them On Instagram

With 1 billion users worldwide, Instagram is the biggest social media platform that exists today. It is not just a place where you post pictures anymore, it has become a great place to reach your target audience and build brand awareness as well. It can be difficult to know what works best in terms of content strategy if you haven’t first analyzed your growth on Instagram. Below we will discuss how to use analytics for insights into what type of posts generate engagement so that you know which content you should create in the future. Analytics are important because they help us determine what to do and what not to do. They also help us determine how well we are doing so if you would like to learn how to use analytics, here is a breakdown that explains it all:  

Why is Instagram good for marketing?

Instagram is a good place for marketing because your target audience is on the platform and it can generate money if targeted correctly. Instagram is the ideal place to target millennials who have a whopping $1.3 trillion annual purchasing power in the United States alone, making this the group with the largest spending power of any generation. This number is still expected to grow as millennials were expected to make up 50% of the workforce by 2023. These statistics display the potential amount of customers Instagram has at your disposal making it a good place for marketing. Altogether Instagram has 1 billion users which is a beautiful number for anybody looking to market anything. The platform makes it possible for you to gain audience insight in the form of Instagram analytics.  

Activity insights

Instagram also makes it possible for you to get to know your demographic through audience analytics. These analytics will inform you about the top location, age range as well as gender of your audience. You will also have access to your follower hours which tells you the average time of day your demographic are on Instagram as well as follower days which tell you the days of the week that your audience is most active. Knowing your audience is very helpful because you get to know who is on the receiving end of your content and allows you to tailor your content as well as posting times according to the information you receive.  

See what kind of posts work

Analytics is a great way to see which posts are popular and which aren’t. You can discover this information by looking at analytics for Instagram feed, stories, reels and IGTV. Looking at these analytics individually will give you a better understanding of which posts are most popular and which posts you should be posting. Knowing these analytics is very helpful because it will tell you which types of posts you should focus on based on their success rate amongst your followers, which helps to

Increase brand awareness

Lastly, we are going to discuss how you can use analytics to increase brand awareness on Instagram. To do this you simply need to focus on the following metrics:

Follower count: The more followers you have means more brand awareness.

Impressions: The more people who see your stories and posts the better for your brand or business.

Reach: Tracks the number of views on your stories, posts and your account. Growing your Instagram reach is an important part of growing brand awareness.

Hql Commands For Data Analytics

HQL or Hive Query Language is a simple yet powerful SQL like querying language which provides the users with the ability to perform data analytics on big datasets. Owing to its syntax similarity to SQL, HQL has been widely adopted among data engineers and can be learned quickly by people new to the world of big data and Hive.

In this article, we will be performing several HQL commands on a “big data” dataset containing information on customers, their purchases, InvoiceID, their country of origin and many more. These parameters will help us better understand our customers and make more efficient and sound business decisions.

For the purpose of execution, we would be using Beeline command Line Interface which executes queries through HiveServer2.

Next, we type in the following command which connects us to the HiveServer2.

It requires authentication so we input the username and password for this session and provide the location or path where we have our database stored. The commands(underlined in red) for this are given below.

set chúng tôi = /user/username/warehouse;

Now that we are connected to HiveServer2 we are ready to start querying our database. Firstly we create our database “demo01” and then type in the command to use it.

Use demo01;

Now we are going to list all the tables present in the demo01 database using the following command

show tables;

As we can see above 2 tables “emp” and “t1” are already present in the demo01 database. So for our customer’s dataset, we are going to create a new table called “customers”.

CREATE TABLE IF NOT EXISTS customers (InvoiceNo VARCHAR(255),Stock_Code VARCHAR(255),Description VARCHAR(255),Quantity INT,UnitPrice DECIMAL(6,2),CustomerID INT,Country VARCHAR(255)) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' ;

Now if we run the “show tables” command we see the following output.

We can see that a table named customers has been created in the demo01 database. We can also see the schema of the table using the following command.

desc customers;

Now we upload our chúng tôi file containing customer records to our hdfs storage using this command.

hdfs dfs -put chúng tôi /user/itv001775/warehouse/demo01.db/customers

Now we have to load this data into the customer’s table we created above. To do this we run the following command.

load data inpath '/user/itv001775/warehouse/demo01.db/customers/new.csv' into table customers;

This concludes the part where we have uploaded the data on hdfs and loaded it into the customer’s table we created in the demo01 database.

Now we shall do a bit of data eyeballing meaning to have a look at the data and see what insights can be extracted from it. As the dataset contains over 580,000 records we shall have a look at the first 5 records for convenience using this command.

select * from customers limit 5;

We can see above it has 7 columns namely invoiceno, stock_code, description, quantity, unitprice,  customerid and country. Each column brings value and insights for the data analysis we are going to perform next.



stock code 3427AB, quantity 2 at a unit price of 9000.


insert into customers values (‘610221’,’3427AB’,’Gaming PC’,2,9000,18443,’Germany’);

Now we can query the database to see if the record was inserted successfully.

select * from customers limit 5;

As we can see record has been inserted.

CASE 2: We want to see the sum of the purchases made by each customer along with invoiceno in descending order.

QUERY: (for convenience we limit our output to the first 10 records)

select customerid, sum(unitprice) as total_purchase from customers group by customerid order by total_purchase desc limit 10;

In the above query, we are grouping our records together based on the same customers id’s and ordering the results by total purchase made by each customer.

Apart from the customers without a customerid, we are able to find out our top 10 customers according to the amount of their purchase. This can be really helpful in scouting and targeting potential customers who would be profitable for businesses.

CASE 3: We want to find out the average price of bags being sold to our customers.


select avg(unitprice) as average_bagPrice from customers where description like '%BAG%';

Note that in the above query we used the “like” logical operator to find the text from the description field. The “%” sign represents that anything can come before and after the word “bag” in the text.

We can observe that the average price across the spectrum of products sold under the tag of bags is 2.35(dollars, euros or any other currency). The same can be done for other articles which can help companies to determine the price ranges for their products for better sales output.

price of products for top 10 countries in descending order by count.


select count(*) as number_of_products, sum(unitprice) as totalsum_of_price, country from customers group by country order by totalsum_of_price desc limit 10;

Here count(*) means counting all the records separately for each country and ordering the output by total sum of price of goods sold in that country.

Through this query, we can infer the countries the businesses should target the most as the total revenue generated from these countries is maximum.

quantity for top 20 customers.


For each customer, we are grouping their records by their id and finding the number of products they bought in descending order of that statistic. We also employ the condition that only those records are selected where a number of products are greater than 10.

Note that we always use the “having” clause with the group by when we want to specify a condition.

Through this, we can see our top customers based on the number of products they ordered. The customers ordering the most generate the most amount of profit for the company and thus should be scouted and pursued the most, and this analysis helps us find them efficiently.


Hive has an amazing feature of sorting our data through the “Sort by” clause. It almost does the same function as the “order by” clause as in they both arrange the data in ascending or descending order. But the main difference can be seen in the working of both these commands.

We know that in Hive, the queries we write in HQL are converted into MapReduce jobs so as to abstract the complexity and make it comfortable for the user.

So when we run a query like :

Select customerid, unitprice from customers sort by customerid;

Multiple mappers and reducers are deployed for the MapReduce job. Give attention to the fact that multiple reducers are used which is the key difference here.

Multiple mappers output their data into their respective reducers where it is sorted according to the column provided in the query, customerid in this case. The final output contains the appended data from all the reducers, resulting in partial sorting of data.

Whereas in order by multiple mappers are used along with only 1 reducer. Usage of a single reducer result in complete sorting of the data passed on from the mappers.

Select customerid, unitprice from customers order by customerid;

The difference in the Reducer output can be clearly seen in the data. Hence we can say that “order by” guarantees complete order in the data whereas “sort by” delivers partially ordered results.


In this article, we have learned to run HQL queries and draw insights from our customer dataset for data analytics. These insights are valuable business intelligence and are very useful in driving business decisions.

Read more articles on Data Analytics here.


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