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In 2023, augmented analytics, artificial intelligence along with resolute memory server will continue to be the hallmark emerging competitive trends and it is expected to drive innovation over the next couple of years. Gartner Analysts Donald Feinberg and Rita Sallam, in their presentation at Gartner Data and Analytics Summit held in Sydney, released the list of leading trends in data and analytics that have the capability to transform the business environment.Augmented Analytics
The novel archetype of augmented analytics has emerged out in the market. The core of this technology is the use of machine learning automation to enhance human intelligence and augment entire data and analytics workflow. The technology is crucial for impartial contextual awareness, acting on insights and unbiased decisions. It is expected that by 2023,Augmented Data Management
Augmented Data Management performs suitable machine learning and artificial intelligence capabilities for vital information management tasks. The process, as explained by Gartner, is “self-configuring and self-tuning”. This augmentation is likely to impactContinuous Intelligence
Continuous Intelligence is a new age machine driven pavement to analytics which enables the user to get to all data and pace up the analysis regardless of a variety of data sources and vast volumes. This approach lets the machine to automate continuously and frictionless. The projection of the technology by 2023 depicts more than 50 percent of major new business systems will employ continuous intelligence using real-time context data for improvisation of decisions.Explainable AI
Explainable AI (or Transparent AI) is that category of artificial intelligence whose actions are trustworthy and understandable by humans.Graph Analytics
The surge of innovation enveloping big data has introduced the world with a number of open source graph databases to execute hassle-free analysis. Arguably, many of the prevailing techniques are unable to handle the volumes and velocities of big data. In light of the solution to this issue, graph analytics leverage the technique of analyzing focus data to the real world and identify key factors influencing the graphical trend. Over 75 percent of big organizations are expected to employ the technology for analyzing forensic behavior, privacy and customer trust issue and to reduce brand and reputation risk.Data Fabric
Data fabric is not a newly-tossed term; its relevance has been there over the years in the industry. When organizations struggle to integrate their data into a single, scalable platform, data fabric provides a comprehensive approach to hit the mark. With the expansion of companies and their data usage, there is a need for a better data solution at this hour. The forecast illustrates that the requisition of graph processing and graph database will accelerate at 100 percent annually by 2023. The technology will enhance data preparation and enable more complex and adaptive data science.NLP and Conversational Analytics
Natural Language Processing and Conversational User Interface is an interesting way of interacting with devices either using phones, smart home assistants (Alexa and Google Home) or any IoT devices. Regardless of the format, the AI-powered conversational analytics assistant is capable of interpreting dictated commands by the user. Data management manual tasks will be minimized by 45 percent by the addition of such technology through 2023 adding up to automated service level management.Commercial AI and Machine Learning
Artificial Intelligence and Machine Learning have marked their impression in the industry in the past few years and continue to grow at a high pace to revolutionize the lives we live. Earlier Gartner report predicted that in the next 2 years, artificial intelligence will become investment priorities for approximately 30 percent of C-suite executives. Additionally, by 2023, customized designs of data tools will be introduced as static infrastructure which will enhance the redesigning for dynamic approaches.Blockchain
Since the disruptive inception, blockchain technology has provided the global community with transformed and novel solutions in certain areas including finance, authentication, and data security. The very technology created by Satoshi Nakamoto is a distributed ledger platform which enables the information sharing (not copied) across a network. Most sanctioned blockchain uses are going to be replaced by ledger DBMS products by 2023, says Gartner release.Persistent Memory Server
In 2023, augmented analytics, artificial intelligence along with resolute memory server will continue to be the hallmark emerging competitive trends and it is expected to drive innovation over the next couple of years. Gartner Analysts Donald Feinberg and Rita Sallam, in their presentation at Gartner Data and Analytics Summit held in Sydney, released the list of leading trends in data and analytics that have the capability to transform the business chúng tôi novel archetype of augmented analytics has emerged out in the market. The core of this technology is the use of machine learning automation to enhance human intelligence and augment entire data and analytics workflow. The technology is crucial for impartial contextual awareness, acting on insights and unbiased decisions. It is expected that by 2023, augmented analytic s will act as an authoritative catalyst of the newest form of trade among analytics and business intelligence.Augmented Data Management performs suitable machine learning and artificial intelligence capabilities for vital information management tasks. The process, as explained by Gartner, is “self-configuring and self-tuning”. This augmentation is likely to impact data management software which included data quality, metadata management, master data management, data integration, and database management systems. In the next 2 years, half of the analytical queries will be created through search, natural language processing or voice.Continuous Intelligence is a new age machine driven pavement to analytics which enables the user to get to all data and pace up the analysis regardless of a variety of data sources and vast volumes. This approach lets the machine to automate continuously and frictionless. The projection of the technology by 2023 depicts more than 50 percent of major new business systems will employ continuous intelligence using real-time context data for improvisation of decisions.Explainable AI (or Transparent AI) is that category of artificial intelligence whose actions are trustworthy and understandable by humans. Explainable AI or XAI is deployed for implementation of a social and rightful explanation of actions. The debate still goes on over – if artificial intelligence can be smart or transparent because of the increasing complexity of internal AI. Despite all if’s and but’s, it can be foreseen that by 2023, 75 percent of new end-user solutions anchoring AI/ML techniques will be designed with commercial platforms rather than open-source chúng tôi surge of innovation enveloping big data has introduced the world with a number of open source graph databases to execute hassle-free analysis. Arguably, many of the prevailing techniques are unable to handle the volumes and velocities of big data. In light of the solution to this issue, graph analytics leverage the technique of analyzing focus data to the real world and identify key factors influencing the graphical trend. Over 75 percent of big organizations are expected to employ the technology for analyzing forensic behavior, privacy and customer trust issue and to reduce brand and reputation chúng tôi fabric is not a newly-tossed term; its relevance has been there over the years in the industry. When organizations struggle to integrate their data into a single, scalable platform, data fabric provides a comprehensive approach to hit the mark. With the expansion of companies and their data usage, there is a need for a better data solution at this hour. The forecast illustrates that the requisition of graph processing and graph database will accelerate at 100 percent annually by 2023. The technology will enhance data preparation and enable more complex and adaptive data science.Natural Language Processing and Conversational User Interface is an interesting way of interacting with devices either using phones, smart home assistants (Alexa and Google Home) or any IoT devices. Regardless of the format, the AI-powered conversational analytics assistant is capable of interpreting dictated commands by the user. Data management manual tasks will be minimized by 45 percent by the addition of such technology through 2023 adding up to automated service level management.Artificial Intelligence and Machine Learning have marked their impression in the industry in the past few years and continue to grow at a high pace to revolutionize the lives we live. Earlier Gartner report predicted that in the next 2 years, artificial intelligence will become investment priorities for approximately 30 percent of C-suite executives. Additionally, by 2023, customized designs of data tools will be introduced as static infrastructure which will enhance the redesigning for dynamic approaches.Since the disruptive inception, blockchain technology has provided the global community with transformed and novel solutions in certain areas including finance, authentication, and data security. The very technology created by Satoshi Nakamoto is a distributed ledger platform which enables the information sharing (not copied) across a network. Most sanctioned blockchain uses are going to be replaced by ledger DBMS products by 2023, says Gartner chúng tôi or Persistent Memory is a solid-state high performance (byte) memory devices which allow the device to have DRAM like access to data. The technology has the same speed and abeyance of DRAM and the non-volatility of NAND flash. Non-volatile dual in-line memory module and Optane DC persistent memory modules are two prototypes of the technology. Persistent memory will serve around 10 percent of in-memory computing memory GB consumption by the year 2023.
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Data analytics jobs have been well paid and in high demand for some time.
The “IT Skills and Certifications Pay Index” by Foote Partners shows that such skills often merit a pay premium, and the average salary of these specialists has been steadily rising. Among the high-paying areas currently are risk analytics, big data analytics, data science, prescriptive analytics, predictive analytics, modeling, Apache Hadoop, and business analytics.
But data analytics is a broad term. It encompasses business intelligence (BI) and visualization as well as the application of analytics to other functions, such as IT and cybersecurity.
Here are some of the five top trends in data analytics jobs:
See more: The Data Analytics Job Market
Experience or certification in a specific programming language or analytics discipline used to be a passport to good jobs. It will still gain people some positions, but they need more if they hope to move up the pay scale.
“For analytics professionals, listing proficiency in SAS, Python, or R may get someone past the initial HR screening, but that’s about it,” said Sean O’Brien, SVP of education at SAS.
Data analytics candidates need experience, certification, and other human skills to succeed in today’s market.
It used to be enough to crunch some numbers and then tell the business an outcome or prediction using regular language.
These days, executives demand more. A top trend for data analytics jobs is the increasing importance of communication skills and storytelling. The rise of chief data officers and chief analytics officers is the clearest indication that analytics has moved from the backroom to the boardroom, and more often, it’s data experts that are setting strategy.
“The ability to make analytics outputs relatable to stakeholders across the business will set them apart,” said O’Brien with SAS.
“It’s not enough to be able to clean, integrate, and analyze huge amounts of data. Analytics pros have to understand how data and analytics directly support business goals and be able to communicate the story the data is telling. They need to be able to not just present trends and reports but communicate their meaning.”
Cybersecurity trends apply to data analytics in two ways: Analysts need to be aware of and possess some security skills if they are to keep their platforms and models secure. But perhaps even more importantly, analytics jobs are becoming available in greater frequency in security. Analysts are needed who can unlock the vast troves of data available in system logs, alerts, and organizational data to find the potential incursions and isolate threats.
“Flexibly and securely viewing trusted data in context through shared applications across an industry ecosystem also enables process and governance improvement,” said Jeffrey Hojlo, an analyst at IDC.
Storage, too, has transitioned into the analytics arena. Storage administrators are spending less time managing storage devices and more time managing data. This entails being more strategic about data mobility, data management, data services, and delivering the foundation for generating value from unstructured data.
“Storage administrators must leverage analytics about files, such as types of files, access times, owners, and other attributes,” said Randy Hopkins, VP of global systems engineering and enablement at Komprise.
See more: Top Data Analytics Certifications
Risk is a hot area across the business world. And it is up to risk management and risk analysts to identify, analyze, and accept or mitigate any uncertainty that may exist in business or investment decisions.
A variety of tactics are used to determine risk. For example, a common tool is known as standard deviation, which is a statistical measure where data is plotted around a central tendency. Management can then see how much risk might be involved and how to minimize that risk.
Those skilled in modern risk analytics are now in greater demand, as the risk management field transitions from manual or traditional methods. Accordingly, risk analytics and risk assessment jobs rose by 5.3% in value over a six-month period, according to surveys by Foote Partners. This form of business intelligence exploits structured and unstructured data as a way to model scenarios and outcomes and provide insight into potential fraud, market risk, credit risk, financial risk, supply chain risk, and other areas of risk.
As a sign that there was definite substance to the hype around big data, Foote Partners notes that big data analytics jobs continue to be in demand. They have risen in value by 13.3% over a six-month period.
See more: 10 Top Companies Hiring for Data Analytics Jobs
Why are Transformers deemed as an Upgrade from RNNs and LSTM?
Artificial intelligence is a disruptive technology that finds more applications each day. But with each new innovation in artificial intelligence technologies like machine learning, deep learning, neural network, the possibilities to scale a new horizon in tech widens up. In the past few years, a form of neural network that is gaining popularity, i.e., Transformers. They employ a simple yet powerful mechanism called attention, which enables artificial intelligence models to selectively focus on certain parts of their input and thus reason more effectively. The attention-mechanism looks at an input sequence and decides at each step which other parts of the sequence are important.
Artificial intelligence is a disruptive technology that finds more applications each day. But with each new innovation in artificial intelligence technologies like machine learning, deep learning, neural network, the possibilities to scale a new horizon in tech widens up. In the past few years, a form of neural network that is gaining popularity, i.e., Transformers. They employ a simple yet powerful mechanism called attention, which enables artificial intelligence models to selectively focus on certain parts of their input and thus reason more effectively. The attention-mechanism looks at an input sequence and decides at each step which other parts of the sequence are important. Basically, it aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease. Considered as a significant breakthrough in natural language processing (NLP) , its architecture is a tad different than recurrent neural networks (RNN) and Convolutional Neural Networks (CNNs). Prior to its introduction in a 2023 research paper , the former state-of-the-art NLP methods had all been based on RNN (e.g., LSTMs). RNN typically processes data in a loop-like fashion (sequentially), allowing information to persist. However, the problem with RNN is that in case the gap between the relevant information and the point where it is needed becomes very large, the neural network becomes very ineffective. This means, RNN becomes incapable of handling long sequences like gradient vanish and long dependency. To counter this, we have attention and LSTM mechanisms. Unlike RNN, LSTM leverages, Gate mechanism to determine which information in the cell state to forget and which new information from the current state to remember. This enables it to maintain a cell state that runs through the sequence. It also allows, it to selectively remember things that are important and forget ones not so important. Both RNNs and LSTM are popular illustrations of sequence to sequence models. In simpler words, Sequence-to-sequence models (or seq2seq) are a class of machine learning models that translates an input sequence to an output sequence. Seq2Seq models consist of an Encoder and a Decoder. The encoder model is responsible for forming an encoded representation of the words (latent vector or context vector) in the input data. When a latent vector is passed to the decoder, it generates a target sequence by predicting the most likely word that pairs with the input word for the respective time steps. The target sequence can be in another language, symbols, a copy of the input, etc. These models are generally adept at translation, where the sequence of words from one language is transformed into a sequence of different words in another language. The same 2023 research paper, titled “Attention is All You Need” by Vaswani et al. , from Google, mentions that RNN and LSTM counter the problem of sequential computation that inhibits parallelization. So, even LSTM fails when sentences are too long. While a CNN based Seq2Seq model can be implemented in parallel, and thus reducing time spent on training in comparison with RNN, it occupied huge memory. Transformers can get around this lack of memory by perceiving entire sequences simultaneously. Besides, they enable parallelization of language processing, i.e., all the tokens in a given body of text are analyzed at the same time rather than in sequence. Though the transformer depends on transforming one sequence into another one with the help of two parts (Encoder and Decoder), it still differs from the previously described/existing sequence-to-sequence models. This is because as mentioned above, they employ attention mechanism. The attention mechanism emerged as an improvement over the encoder decoder-based neural machine translation system in natural language processing. It also allows a model to consider the relationships between words regardless of how far apart they are – addressing the long-range dependencies issues. It achieves this by enabling the decoder to focus on different parts of the input sequence at every step of the output sequence generation. Now, dependencies can be identified and modeled irrespective of their distance in the sequences. Unlike previous seq2seq models, Transformers do not discard the intermediate states and nor use the final state/context vector when initializing the decoder network to generate predictions about an input sequence. Moreover, by processing sentences as a whole and learning relationships, they avoid recursion. Some of the popular Transformers are BERT , GPT-2 and GPT-3 . BERT or Bidirectional Encoder Representations from Transformers was created and published in 2023 by Jacob Devlin and his colleagues from Google. OpenAI’s GPT-2 has 1.5 billion parameters, and was trained on a dataset of 8 million web pages. Its goal was to predict the next word in 40GB of Internet text. In contrast, GPT-3 was trained on roughly 500 billion words and consists of 175 billion parameters. It is said that, GPT-3 is a major leap in transforming artificial intelligence by reaching the highest level of human-like intelligence through machine learning. We also have Detection Transformers (DETR) from Facebook which was introduced for better object detection and panoptic segmentation.
Top 10 Trends of Blockchain in 2023 1. Blockchain as a service (Bass) by big tech companies
One of the promising blockchain trends in 2023 is BaaS, short for Blockchain As A Service. It is another blockchain trend that is as of now coordinated with various new businesses just as endeavors.
These computerized items might be shrewd agreements, decentralized applications (Dapps), or considerably different administrations that can work with no arrangement prerequisites of the total blockchain-based foundation.
A portion of the organizations building up a blockchain that give BaaS administration are Microsoft and Amazon, subsequently forming the fate of blockchain applications.2. Combined blockchain moves to the center stage
Blockchain systems can be named: Private, Public, Federated, or Hybrid. The term Federated Blockchain can be alluded to as outstanding amongst other blockchain’s most recent trends in the business.
It is simply an updated type of the essential blockchain model, which makes it progressively perfect for some, particular use cases. In this sort of blockchain, rather than one association, different specialists can control the pre-chosen hubs of blockchain.
Presently, this choice gathering of different hubs will approve the square with the goal that the exchanges can be handled further. In 2023, there will be an ascent in the utilization of combined blockchain as it gives private blockchain systems, a progressively adaptable standpoint.3. Stable coins will be more visible
Utilizing Bitcoin for instance of digital forms of money its exceptionally unstable in nature.
To maintain a strategic distance from that unpredictability stable coin went to the image unequivocally with a stable worth partner with each coin.
Starting at now, stable coins are in their underlying stage and it is anticipated that 2023 will be the year when blockchain will accomplish their unequaled high.
One main impetus for utilizing stable coin is the presentation of Facebook’s digital money “Libra” in 2023 even with all the difficulties confronting this new cryptographic money proposed by Facebook and the contracting circle of accomplices in libra.org.4. Long-range informal communication problems meet blockchain solution
There are around 2.77 Billion online life clients around the world in 2023.
The presentation of blockchain in online life will have the option to tackle the issues identified with infamous embarrassments, security infringement, information control, and substance significance.
Consequently, the blockchain mix in the internet based life area is another rising innovation trend in 2023.
With the execution of blockchain, it very well may be guaranteed that all the internet based life distributed information stay untraceable and can’t be copied, much after its erasure.
Also, clients will get the chance to store information all the more safely and keep up their possession.
Blockchain likewise guarantees that the intensity of substance pertinence lies in the possession of the individuals who made it, rather than the stage proprietors.
This causes the client to feel increasingly make sure about as they can control what they need to see. One overwhelming undertaking is to persuade online life stages to actualized it, this can be on a willful base or as a consequence of security laws like GDPR.5. Interoperability and blockchain networks
Blockchain interoperability is the capacity to share information and other data over numerous blockchain frameworks just as systems. This capacity makes it basic for people, in general, to see and access the information across various blockchain systems.6. Economy and finance will lead blockchain application
In contrast to other customary organizations, the banking and fund businesses don’t have to acquaint radical change with their procedures for embracing blockchain innovation.
After it was effectively applied for the cryptographic money, budgetary establishments start genuinely considering blockchain appropriation for customary financial tasks.
PWC report, 77 percent of monetary organizations are relied upon to receive blockchain innovation as a feature of an underway framework or procedure by 2023.
Blockchain innovation will permit banks to lessen inordinate organization, lead quicker exchanges at lower costs, and improve its mystery.
One of the blockchain forecasts made by Gartner is that the financial business will infer 1 billion dollars of business esteem from the utilization of blockchain-based cryptographic forms of money by 2023.
In addition, blockchain can be utilized for propelling new cryptographic forms of money that will be managed or impacted by financial strategy.
Along these lines, banks need to decrease the upper hand of independent cryptographic forms of money and accomplish more noteworthy power over their fiscal arrangement.7. Blockchain integration into government agencies
The possibility of the circulated record is additionally exceptionally appealing to government specialists that need to administrate extremely huge amounts of information.
As indicated by Gartner, by 2023, in excess of a billion people will have a little information about them put away on a blockchain, yet they may not know about it.8. Blockchain combines with IOT
The IoT tech market will consider a to be center around security as intricate wellbeing challenges crop up. These complexities originate from the different and conveyed nature of the innovation.
The quantity of Internet-associated gadgets has penetrated the 26 billion imprint. Gadget and IoT arrange hacking will get ordinary in 2023. It is up to arrange administrators to prevent interlopers from doing their business.
The current brought together the design of IoT is one of the fundamental purposes behind the defenselessness of IoT systems.
With billions of gadgets associated and more to be included, IoT is a major objective for digital assaults, which makes security-critical. Blockchain offers new trust in IoT security for a few reasons.
To begin with, blockchain is open, everybody taking an interest in the system of hubs of the blockchain system can see the squares and the exchanges put away and endorses them, in spite of the fact that clients can, in any case, have private keys to control exchanges.
Second, blockchain is decentralized, so there is no single position that can support the exchanges dispensing with Single Point of Failure (SPOF) shortcoming. Third and above all, it’s safe—the database must be broadened and past records can’t be changed.
Numerous IoT based organizations receive blockchain innovation for their business arrangements. The International Data Corporation (IDC) is expecting that 20 percent of IoT arrangements will empower blockchain administrations by 2023.9. Blockchain with AI
With the joining of AI (Artificial Intelligence) with blockchain innovation will make for a superior turn of events. This combination will show a degree of progress in blockchain innovation with a sufficient measure of utilizations.
The International Data Corporation (IDC) proposes that worldwide spending on AI will reach $57.6 billion by 2023 and 51% of organizations will make the change to AI with blockchain combination.
Moreover, blockchain can likewise make AI progressively rational and justifiable, and we can follow and decide why choices are made in AI. Blockchain and its record can record all information and factors that experience a choice made under AI.
In addition, AI can help blockchain productivity much better than people, or even standard processing can. A gander at the manner by which blockchains are at present sudden spike in demand for standard PCs demonstrates this with a great deal of preparing power expected to perform even fundamental errands.10. Interest for blockchain experts
Blockchain is another innovation and there is just not many percents of people who are talented in this innovation.
As blockchain innovation turning into a quickly expanding and wide-spreading innovation, that makes a circumstance for some to create aptitudes and experience about blockchain innovation.
Despite the fact that the quantity of specialists in blockchain fields is expanding, then again the execution of this innovation has a fast development which will make a circumstance for the interest of Blockchain trend in 2023.Conclusion:
It merits saying that there are certified endeavors by colleges and universities to find this need, however, the pace of graduating understudies with enough abilities to manage blockchain innovation isn’t sufficient to fill the hole.
Additionally, companies are finding a way to expand on their current abilities by including preparing programs for creating and overseeing blockchain systems.
Google has released new behavioural insights into 2023 holiday shoppers, with tips on how to sell to US consumers.
According to Google’s data, mobile searches for “best deals” have grown by 90%. So it should come as no surprise that the #1 factor when consumers decide where to buy is whichever retailer has the lowest price.
Consumers also appreciate being able to do what they want all on their own. Around 70% of shoppers say they like it when companies make it easy for them to do what they want without having to talk to anyone
It’s also interesting to note that searches around “rewards apps” and “Black Friday deals” are up 200% this year. Although consumers are getting their shopping started before Black Friday hits – 37% of holiday shopping has been completed before the week of Black Friday and Cyber Monday.
Here’s a look at what influences US consumers most when buying goods online.Buying Decisions: Most Influential Factors
Google separated its data into three categories: hard goods, soft goods, and everyday essentials. The most influential factors are largely similar across all categories.
Here’s what influences US consumers most:
Sales, discounts, & promos
Estimated delivery date
Cross-store price comparisons
These factors were found to be least influential for US consumers:
Popularity on social media
Ability to share product pages
Ability to chat with the merchant
Non-credit card payments
Reviews from family/friends
Loyalty rewards program
Customer store reviews
Brand/company based in-country
This data can be used to guide your ecommerce marketing strategy with regards to where you should focus your efforts.
For example, highlighting the fact that you offer lower prices than competitors is more important than emphasizing you’re a US-based company. As Google says, “When you can beat the competition, make sure you show it.”
Above all, Google recommends aiming for fast and free shipping when selling to US consumers. Consumers in the US are much more interested in fast shipping times than in-store pickup.
For more data, see Google’s full study here. Also check out Search Engine Journal’s Complete Guide to Holiday Marketing.
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 commandshow 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.DATA ANALYSIS THROUGH HQL
stock code 3427AB, quantity 2 at a unit price of 9000.
QUERY: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.
QUERY: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.
QUERY: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.BONUS
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.ENDNOTES
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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