Trending March 2024 # How Cybersecurity Transforms Ai And Big Data Analytics In Healthcare # Suggested April 2024 # Top 6 Popular

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Healthcare Industry has been one of the significant grantees of modern technologies embracing Artificial Intelligence (AI) and Big Data in the sector. The ever-growing efficiency of these technologies has a lot to offer in the field specializing in concerning health results, reduced costs and improved convenience. We can clearly witness the optimistic flourishing of healthcare sector at prompt velocity including managing patient health, curing diseases and others. Realizing the need of the hour, the

Technology Magnifying Healthcare Follow-ups

Malignant players are becoming agile when it comes to targeting medical management, which makes cybersecurity a big issue. As per survey data reports, 90% of hospitals have experienced a cyber-attack in the time span of the last five years. Although, with the growth of AI and

Malicious Software Diagnosis

When it comes to malware detection, machine learning applications play a critical role. As most of these applications are architected to indicate risk using historical data and malware pattern, they can briskly detect alarming threats against the healthcare sector.  

Retaliation of Security Violation

Artificial intelligence has better efficiency to eliminate the risks after a security breach when compared to conventional models. AI can potentially identify oddities in the nexus. Subsequently, the issue is then forwarded to human insight for further operations. Additionally, traffic signals can be categorized to separate sensitive data on individual security protocol using AI-powered automation.  

Uproot the Risk Factor

Smart gadgets are more vulnerable to being hacked but AI is helpful in this area too. Data encryption, particularly for malware detection, can be implemented using AI which can set medical management teams free from depending over makers to ensure if security is upgraded.

Healthcare Industry has been one of the significant grantees of modern technologies embracing Artificial Intelligence (AI) and Big Data in the sector. The ever-growing efficiency of these technologies has a lot to offer in the field specializing in concerning health results, reduced costs and improved convenience. We can clearly witness the optimistic flourishing of healthcare sector at prompt velocity including managing patient health, curing diseases and others. Realizing the need of the hour, the healthcare industry is accepting tech innovations at a rapid pace to meet the rising demands. Since everything has its own pros and cons, similarly cybersecurity has become a haunting concern in critical healthcare sector regarding patient health. Let’s have a brief look at the rhythm of Cybersecurity, AI and Big Data’s role in it.Malignant players are becoming agile when it comes to targeting medical management, which makes cybersecurity a big issue. As per survey data reports, 90% of hospitals have experienced a cyber-attack in the time span of the last five years. Although, with the growth of AI and Big Data , safeguarding the patient data has become comparatively chúng tôi it comes to malware detection, machine learning applications play a critical role. As most of these applications are architected to indicate risk using historical data and malware pattern, they can briskly detect alarming threats against the healthcare sector.Artificial intelligence has better efficiency to eliminate the risks after a security breach when compared to conventional models. AI can potentially identify oddities in the nexus. Subsequently, the issue is then forwarded to human insight for further operations. Additionally, traffic signals can be categorized to separate sensitive data on individual security protocol using AI-powered automation.Smart gadgets are more vulnerable to being hacked but AI is helpful in this area too. Data encryption, particularly for malware detection, can be implemented using AI which can set medical management teams free from depending over makers to ensure if security is upgraded. Having excitement regarding innovations in quite natural but the anticipation of risk factor coming along with it should be kept in mind. However, these circumstances make it important for healthcare administrators, physicians and patients to be cautious and candid to retain secure progress in the near future.

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How Big Data Analytics Is Redefining Bfsi Sector

Big data analytics is proliferating fast in almost all the industries including banking and securities, communications, media and entertainment, healthcare and education, to name a few. There are numerous organizations who have included big data analytics as part of their growth strategies. Those businesses are no less than role models for others. In this article, we will be specifically emphasizing on the Banking Financial Services and Insurance (BFSI) sector. A few decades ago, banking processes were transformed by IT systems. These days, it is the big data analytics that is facilitating banks and financial businesses to make them compliant, which is undoubtedly putting them one step forward to their competitors. Big data analytics helps to monitor enormous datasets to uncover market developments, consumer likings, data interactions, and other insights which assist in strategic planning of BFSI organizations. Big data analytics is helping BFSI sector in some of the following noted ways: 1) Predictive Analytics The past transaction records of any bank or financial institution can be used as an effective input for forecast and future strategic planning. Big data can benefit companies to track market developments and plan future targets. The analysis can also be interpreted to highlight the risks associated with day to day work of an organization. 2) Faster Data Processing For businesses with a large dynamic customer database, traditional data management systems aren’t fully-flavored. The traditional system is also deficient to handle the multi-dimensionality of big data. By switching to data analytics platforms, banks would be able to handle gigantic quantities of data seamlessly. 3) Performance Analytics Banks can customize big data analytics to monitor business and employee performance and then work accordingly on budgets and employee KPI’s grounded on previous accomplishments. Moreover, they can mark training and education of employees and monitor performance in the direction of targets in real time. As a result, banks can make their product more trustworthy to their customers with maximum utilization of resources. 4) Fraud and Malicious Attack Protection The increased technological usage has given birth to plentiful threats for the BFSI sector. Despite having stringent security laws globally, organizations face attacks and threat on a regular basis. With big data analytics tools and techniques, banks are now able to recognize unusual patterns and take business actions accordingly. Big data analytics also supports biometrics which is responsible to create unique ID for every new user. At the same time, online transaction encryption is also a gift of data analytics which is helping the industry in an effective way. 5) Risk Analysis and Management The banking industry is full of risk with every single transaction needs to be witnessed carefully. Business intelligence (BI) and analytics tools are able to give banks new understandings of their structures, dealings, clients and architecture to help them sidestep risks. Banks can evaluate the influences that cause risks in dealing with defaulted borrowers. BI can also make systems crystal clear so that the management can identify internal or external dishonest activities and categorize history to prevent future risk. 6) Customer Analytics Big data analytics tools and techniques provide the BFSI sector with dynamic and updated statistics of their most lucrative customers. It helps them to chart out effective business strategies to entice their customers. Banks can also use evidenced-based data to preserve top notch clients and market them with relevant products. 7) Better Compliance Monitoring and Reporting The government frequently updates its policies and compliance procedures in different industries. These new standards and rules are being implemented periodically. If organizations use the traditional ways to keep a track of these compliances then it might turn up a bit risky. Big data platform can be used to track these developments so that all the governmental policies and rules are followed by the organization. Future at a Glance

Top 10 Online Big Data Analytics Courses To Enroll In 2023

Build a successful career in data analytics with these top 10 online big data analytics courses

Data analysts shouldn’t be confused with data scientists. Although both data analysts and data scientists work with data, what they do with that data, differs. A data analyst helps business leaders with decision-making by finding answers to a set of given questions using data. If you are interested in pursuing this career, these top 10 online big data analytics courses will be perfect for you.

Data Analyst Nanodegree (Udacity)

Udacity’s Data Analyst Nanodegree will teach you all of the knowledge, skills, and tools needed to build a career in big data analytics. In addition to covering both theory and practice, the program also includes regular 1-on-1 mentor calls, an active student community, and one-of-a-kind career support services. This program is best suited for students who have working experience with Python (and in particular NumPy and Pandas) and SQL programming. However, don’t be discouraged if you lack these prerequisites. There’s also a similarly structured beginner-level Nanodegree, “Programming for Data Science “, that is the perfect pick for you if you don’t meet the prerequisites for this program. The beginner-level program covers just what you need: the basics of Python programming from a data science perspective.

Big Big Data Analytics with Tableau (Pluralsight)

Pluralsight’s Big big data analytics with Tableau will not only give you a better understanding of big data but will also teach you how to access big data systems using Tableau Software. The course covers topics like big big data analytics and how to access and visualize big data with Tableau. The course is taught by Ben Sullins, who has 15 years of industry experience and has offered consulting services to companies like Facebook, LinkedIn, and Cisco. Sullins passes on his knowledge to students through bite-sized chunks of content. As such, students can personalize their learning to suit their individual requirements. You can finish the course in just a day or take your time and complete it over the course of a week or two. This course isn’t necessarily for beginners. You’re expected to have some experience with big data analytics. If you’re a complete beginner, consider taking Data Analysis Fundamentals with Tableau, another course authored by Sullins.

The Data Science Course 2023: Complete Data Science Bootcamp (Udemy)

Available on Udemy, Data Science Course 2023: Complete Data Science Bootcamp is a comprehensive data science course that consists of 471 lectures. The lectures include almost 30 hours of on-demand video, 90 articles, and 154 downloadable resources. While the course is not new and we first covered it back in 2023, it has been updated for 2023 with new learning materials. As part of this course, students can expect to learn in-demand data science skills, such as Python and its libraries (like Pandas, NumPy, Seaborn, and Matplotlib), machine learning, statistics, and Tableau. Although the course might seem a bit overwhelming at first glance, it’s actually well-structured and requires no prior experience. All you need to get started is access to Microsoft Excel. The course will set you back a few hundred dollars. However, since Udemy runs generous discounts fairly regularly, you can get the course for under $20. Either way, this course is a steal, especially considering that you get full lifetime access to it and any future updates.

Become a Data Analyst (LinkedIn Learning)

This particular path consists of seven courses: Learning big data analytics, Data Fluency: Exploring and Describing Data, Excel Statistics Essential Training: 1, Learning Excel: Data Analysis, Learning Data Visualization, Power BI Essential Training, and Tableau Essential Training (2024.1). Each course varies in length. However, most courses are between two and four hours long so that you can complete the entire path from start to finish in about 24 hours. There are no prerequisites to starting this learning path. Infact, you don’t even need to know what data analysis is. The course begins by defining data analysis before teaching you how to identify, interpret, clean, and visualize data. The curriculum is taught via video by six different instructors, all of which are experts in the industry. Some courses include quizzes and every course has a Q&A section where you can ask the lecturer questions about the course. The only downside? There are no hands-on projects.

Big data analytics Bootcamp (Springboard) Data Analyst with R (DataCamp)

This program features bite-sized learning materials curated by data industry experts. It will help you get your dream job in data analysis regardless of how much free time you have to study. DataCamp’s Data Analyst with R Career Track consists of 19 data science analytics courses handpicked by industry experts to help you start a new career in data science. Since each course is about 4 hours long, the entire track should take about 77 hours to complete. At the end of this track, students should be able to manipulate and analyze data using R.

Big Data Analytics Immersion (Thinkful)

With a customized schedule, 1-on-1 mentorship, and 24/7 support from instructors, this course is as close to personalized learning as you can get. Thinkful’s big data analytics Immersion is an intensive full-time training program. Although one of the more expensive big data analytics courses out there (it costs $12,250), it promises to take you from beginner to expert in just four months. However, students are expected to spend between 50 to 60 hours a week studying. Once you sign up for the course, you receive a customized schedule to help you stay on track. The curriculum consists of seven areas: Excel Foundations, Storytelling with Data, SQL Foundation, Tableau, Business Research, Python Foundations, and Capstone Phase. During the Capstone Phase, students not only get to build a final project but also complete two culture fit interviews.

Data Science Specialization (Coursera)

Data Science Specialization offered by Coursera, together with the prestigious John Hopkins University, is a ten-course program that helps you understand the whole data science pipeline at a basic level. Although anyone can sign up for this course, students should have beginner-level experience in Python and some familiarity with regression. The curriculum is taught through videos and complementary readings. Student knowledge is tested via auto-graded practice quizzes and peer-graded assignments. The program culminates with a hands-on project that gives students a chance to create a usable data product.

Business Analytics Specialization (Coursera)

This five-course series aims to teach students how to use big data to make data-driven business decisions in the areas of finance, human resources, marketing, and operations. Created by the Wharton School of the University of Pennsylvania and hosted on Coursera, the Business Analytics Specialization is divided into four discipline-specific courses (customer, operations, people, and accounting analytics). The final, fifth, course, is dedicated to a capstone project. The Specialization is taught through videos and readings. Your knowledge is tested via compulsory quizzes. You can also participate in discussion forums. At the end of the course, students complete a Capstone Project designed in conjunction with Yahoo. The entire Specialization takes about 40 hours to complete, which means that students can finish the program in just six months if they spend three hours a week learning.

Excel to MySQL: Analytic Techniques for Business Specialization (Coursera)

Challenges And Best Practices In Data Analytics

It’s a study in contrast: on one hand, we hear that the power of data analytics is nearly miraculous; the cool, metric-based insight from an our analytics software will propel us to business success. On the other hand is the reality of data analytics in real world organizations: confusion, poorly designed systems, and executives operating by gut instinct rather than data-driven insight.

Why is it so hard for organizations to optimize their data analytics?

To understand the challenges in data analytics – and suggest some best practices – I spoke with four top experts:

Myles Suer, Head of Global Enterprise Marketing, Dell Boomi

Tom Davenport, Professor, Babson College

Marco Iansiti, Professor, the Harvard Business School

Dion Hinchcliffe, Principal Analyst, Constellation Research

Suer: It really depends. MIT CISR did some research a while back, and one of the things they discovered, that I thought was fascinating, was that only 28% of companies were really ready to transform, 51% were still in silos, so the way Tom thinks about it, they’re doing departmental analytics, and 21% were doing things that were duct tape and band-aids. So it’s really interesting. In terms of the use of AI and ML, it’s quite interesting. There are companies like Stitch Fix who are doing kind of amazing things for consumers, but there’s even people like Nordstrom who’ve managed to connect their supply chain, and their purchase data, and actually predict what you want, and I don’t even have that on Amazon. So, there are companies that are succeeding,

Davenport: The International Institute for Analytics does benchmarking of analytics maturity across organizations. And so God had decreed that all analytics maturity… All maturity models should have five levels, and so I complied with that, and level one is really screwed-up, and level five is really sophisticated. Sadly, the average across all companies assessed, and I think there are over 225 so far, is with two-digit, double-digit precision, 2.25.

Hinchcliffe: I do a lot of surveys of CIOs, and one of the top issues, and for a couple years now, analytics has been high in the top five of priorities in terms of fueling it. I just did another CIO survey, it’s number two. Right? Digital transformation’s number one, analytics is number two in terms of priorities. So it’s gotten just a little bit higher.

But it’s like all powerful technologies, analytics separates the leaders from the laggers. And we see it, most organizations are just in the developing phase.

Iansiti: I think right now, we’re in the mode that to do things well, you’re gonna do things at scale, and you do things across a whole variety of different processes. So it’s not about building sort of one cool algorithm to do some prediction in marketing. It’s fundamentally about doing hundreds of these algorithms. I was talking to somebody at Fidelity, for example. They have something like 120 different project leaders that are dedicated to deploying, essentially, some digitally-enabled processes at scale.

Hinchcliffe: I once had a CIO tell me, “My dream is to be able to take everything that we know and make decisions better and faster than our competitors.” Right? “If can deliver that to the table, I can ride that forever.”

Suer: What’s happened in the legacy software world is we’ve required the companies to build their platform themselves. They bought this product over here, and this product over here, and then they had the job of assembling it. And most stumbled along the way and projects became narrower and narrower. So I think the big issue is, is that we need the data scientists, but before we need the data scientists as Marco has implied, we need the data engineers, or we need to somehow acquire something that allows you to go from the initial stage of the data pipeline to clean and pristine data.

Davenport: I think we certainly need data platforms, but we also need kind of workflow and decisioning platforms because, I don’t know, asking people to have a separate step for making their work intelligent, doesn’t seem to be successful. You’ve gotta just really make it easy. So if I’m a salesperson and I’m trying to decide, “Well, who do I call on today to sell my products and services?” You can just pick somebody at random, or you can say, “Oh, okay, we’re using Salesforce, and my boss has kindly bought this Einstein product that gives me a predictive lead scoring model. And gee, why wouldn’t I choose the most likely company to buy my product on a list that’s been prepared for me.” It’s just too easy to ignore, and I think in more and more cases, we’re gonna have to embed analytics and AI into these transactional and decisioning platforms if we’re gonna get them to be used successfully.

Hinchcliffe: I think most organizations are still working on it, and they are… They are in an emerging state, and they’re still having big challenges getting the data from wherever it is to wherever it needs to be, alright. So we have a lot of sclerosis in most organizations, and data ownership is the problem. Data access is an issue. It’s still too siloed. Our data foundations are not in good shape, but I think we’re now seeing the rise of things like customer data platforms, and other solutions that are allowing organizations to systematize, to make data consistent, to make it shareable, because we’re seeing a lot of under-utilization of one of the most valuable and irreplaceable assets in our organizations, which is data, right. And so I think we’ll see basic progress, so that we have stronger open data foundations, and that we’ll also have more skill level in our organization.

We’re gonna have much more sophisticated and workers in five years. They will have that Cloud experience with these now open data platforms with analytics tools. They’re not gonna be doing everything in Excel anymore. They’re gonna be using the next generation of analytics platforms like Snowflake. And so we’ll see… We’ll probably see that human dimension addressed.

Davenport: In terms of what’s happening in the future, I think more use of external data. We have for decades been primarily focused on internal data. If we want to know what’s happening in the world and with people who aren’t our customers yet and so on, we’ve gotta get more external. I think there will be much more availability of external data. You already see some change there. Ironically, maybe a move back to smaller data and smaller models than we have now. There is, I think in this latest AI system called GPT-3 for language creation, 175 billion neuron nodes in this deep-learning model. It’s kind of gone a little too far, one might say. [chuckle]

Iansiti: So they were going to be developing some of these tool sets to organize the data in a way that where the access is much more nuanced than it’s been in the past. And I feel that, also as traditional companies come up to speed on this as they are doing, they’re much more thoughtful and conservative in many ways than digital native companies, they were tiny things just a decade ago. And so from that perspective, I think that what I’m hoping we’ll see in 2025 is a lot more responsible data platform architecture and design. Not that right now everyone’s being irresponsible, but we certainly have some room to grow, I think in that domain I would say, so hopefully.

Suer: But one of the things that’s really interesting, I don’t know if Tom and Marco saw this, but we went from the year I was born and I think Tom was roughly born, from 55 years for the life of an average public company to 20 a few years ago, and last year it dropped to 10-and-a-half years for a public company. So I think the winners are really good at doing analytics and data and things like that, and so the legacy organizations have to figure out quickly how they’re gonna respond or they may become irrelevant to the market

Learn Hive Query To Unlock The Power Of Big Data Analytics

Introduction

Given the number of large datasets that data engineers handle on a daily basis, it is no doubt that a dedicated tool is required to process and analyze such data. Some tools like Pig, One of the most widely used tools to solve such a problem is Apache Hive which is built on top of Hadoop.

Apache Hive is a data warehousing built on top of Apache Hadoop. Using Apache Hive, you can query distributed data storage, including the data residing in Hadoop Distributed File System (HDFS), which is the file storage system provided in Apache Hadoop. Hive also supports the ACID properties of relational databases with ORC file format, which is optimized for faster querying. But the real reason behind the prolific use of Hive for working with Big Data is that it is an easy-to-use querying language.

Apache Hive supports the Hive Query Language, or HQL for short. HQL is very similar to SQL, which is the main reason behind its extensive use in the data engineering domain. Not only that, but HQL makes it fairly easy for data engineers to support transactions in Hive. So you can use the familiar insert, update, delete, and merge SQL statements to query table data in Hive. In fact, the simplicity of HQL is one of the reasons why data engineers now use Hive instead of Pig to query Big data.

So, in this article, we will be covering the most commonly used queries which you will find useful when querying data in Hive.

Learning Objectives

Get an overview of Apache Hive.

Get familiar with Hive Query Language.

Implement various functions in Hive, like aggregation functions, date functions, etc.

Table of Contents Hive Refresher

Hive is a data warehouse built on top of Apache Hadoop, which is an open-source distributed framework.

Hive architecture contains Hive Client, Hive Services, and Distributed Storage.

Hive Client various types of connectors like JDBC and ODBC connectors which allows Hive to support various applications in a different programming languages like Java, Python, etc.

Hive Services includes Hive Server, Hive CLI, Hive Driver, and Hive Metastore.

Hive CLI has been replaced by Beeline in HiveServer2.

Hive supports three different types of execution engines – MapReduce, Tez, and Spark.

Hive supports its own command line interface known as Hive CLI, where programmers can directly write the Hive queries.

Hive Metastore maintains the metadata about Hive tables.

Hive metastore can be used with Spark as well for storing the metadata.

Hive supports two types of tables – Managed tables and External tables.

The schema and data for Managed tables are stored in Hive.

In the case of External tables, only the schema is stored by Hive in the Hive metastore.

Hive uses the Hive Query Language (HQL) for querying data.

Using HQL or Hiveql, we can easily implement MapReduce jobs on Hadoop.

Let’s look at some popular Hive queries.

Simple Selects

In Hive, querying data is performed by a SELECT statement. A select statement has 6 key components;

SELECT column names

FROM table-name

GROUP BY column names

WHERE conditions

HAVING conditions

ORDER by column names

In practice, very few queries will have all of these clauses in them, simplifying many queries. On the other hand, conditions in the WHERE clause can be very complex, and if you need to JOIN two or more tables together, then more clauses (JOIN and ON) are needed.

All of the clause names above have been written in uppercase for clarity. HQL is not case-sensitive. Neither do you need to write each clause on a new line, but it is often clearer to do so for all but the simplest of queries.

Over here, we will start with the very simple ones and work our way up to the more complex ones.

Simple Selects ‐ Selecting Columns

Amongst all the hive queries, the simplest query is effectively one which returns the contents of the whole table. Following is the syntax to do that –

SELECT * FROM geog_all;

It is better to practice and generally more efficient to explicitly list the column names that you want to be returned. This is one of the optimization techniques that you can use while querying in Hive.

SELECT anonid, fueltypes, acorn_type FROM geog_all; Simple Selects – Selecting Rows

In addition to limiting the columns returned by a query, you can also limit the rows returned. The simplest case is to say how many rows are wanted using the Limit clause.

SELECT anonid, fueltypes, acorn_type FROM geog_all LIMIT 10;

This is useful if you just want to get a feel for what the data looks like. Usually, you will want to restrict the rows returned based on some criteria. i.e., certain values or ranges within one or more columns.

SELECT anonid, fueltypes, acorn_type FROM geog_all WHERE fueltypes = "ElecOnly";

The Expression in the where clause can be more complex and involve more than one column.

SELECT anonid, fueltypes, acorn_type FROM geog_all SELECT anonid, fueltypes, acorn_type FROM geog_all

Notice that the columns used in the conditions of the WHERE clause don’t have to appear in the Select clause. Other operators can also be used in the where clause. For complex expressions, brackets can be used to enforce precedence.

SELECT anonid, fueltypes, acorn_type, nuts1, ldz FROM geog_all WHERE fueltypes = "ElecOnly" AND acorn_type BETWEEN 42 AND 47 AND (nuts1 NOT IN ("UKM", "UKI") OR ldz = "--"); Creating New Columns

It is possible to create new columns in the output of the query. These columns can be from combinations from the other columns using operators and/or built-in Hive functions.

SELECT anonid, eprofileclass, acorn_type, (eprofileclass * acorn_type) AS multiply, (eprofileclass + acorn_type) AS added FROM edrp_geography_data b;

A full list of the operators and functions available within the Hive can be found in the documentation.

When you create a new column, it is usual to provide an ‘alias’ for the column. This is essentially the name you wish to give to the new column. The alias is given immediately after the expression to which it refers. Optionally you can add the AS keyword for clarity. If you do not provide an alias for your new columns, Hive will generate a name for you.

Although the term alias may seem a bit odd for a new column that has no natural name, alias’ can also be used with any existing column to provide a more meaningful name in the output.

Tables can also be given an alias, this is particularly common in join queries involving multiple tables where there is a need to distinguish between columns with the same name in different tables. In addition to using operators to create new columns, there are also many Hive built‐in functions that can be used.

Hive Functions

You can use various Hive functions for data analysis purposes. Following are the functions to do that.

Simple Functions

Let’s talk about the functions which are popularly used to query columns that contain string data type values.

Concat can be used to add strings together.

SELECT anonid, acorn_category, acorn_group, acorn_type, concat (acorn_category, ",", acorn_group, ",", acorn_type)  AS acorn_code FROM geog_all;

substr can be used to extract a part of a string

SELECT anon_id, advancedatetime, FROM elec_c;

Examples of length, instr, and reverse

SELECT anonid,      acorn_code,      length (acorn_code),      instr (acorn_code, ',') AS a_catpos,      instr (reverse (acorn_code), "," ) AS reverse_a_typepo

Where needed, functions can be nested within each other, cast and type conversions.

SELECT anonid, substr (acorn_code, 7, 2) AS ac_type_string, cast (substr (acorn_code, 7, 2) AS INT) AS ac_type_int, substr (acorn_code, 7, 2) +1 AS ac_type_not_sure FROM geog_all; Aggregation Functions

Aggregate functions are used to perform some kind of mathematical or statistical calculation across a group of rows. The rows in each group are determined by the different values in a specified column or columns. A list of all of the available functions is available in the apache documentation.

SELECT anon_id,               count (eleckwh) AS total_row_count,               sum (eleckwh) AS total_period_usage,               min (eleckwh) AS min_period_usage,               avg (eleckwh) AS avg_period_usage,              max (eleckwh) AS max_period_usage        FROM elec_c GROUP BY anon_id;

In the above example, five aggregations were performed over the single column anon_id. It is possible to aggregate over multiple columns by specifying them in both the select and the group by clause. The grouping will take place based on the order of the columns listed in the group by clause. What is not allowed is specifying a non‐aggregated column in the select clause that is not mentioned in the group by clause.

SELECT anon_id,               count (eleckwh) AS total_row_count,               sum (eleckwh) AS total_period_usage,               min (eleckwh) AS min_period_usage,               avg (eleckwh) AS avg_period_usage,               max (eleckwh) AS max_period_usage        FROM elec_c

Unfortunately, the group by clause will not accept alias’.

SELECT anon_id,               count (eleckwh) AS total_row_count,               sum (eleckwh) AS total_period_usage,               min (eleckwh) AS min_period_usage,               avg (eleckwh) AS avg_period_usage,               max (eleckwh) AS max_period_usage       FROM elec_c ORDER BY anon_id, reading_year;

But the Order by clause does.

The Distinct keyword provides a set of a unique combination of column values within a table without any kind of aggregation.

SELECT DISTINCT eprofileclass, fueltypes FROM geog_all; Date Functions

Hive provides a variety of date-related functions to allow you to convert strings into timestamps and to additionally extract parts of the Timestamp.

unix_timestamp returns the current date and time – as an integer!

from_unixtime takes an integer and converts it into a recognizable Timestamp string

SELECT unix_timestamp () AS currenttime FROM sample_07 LIMIT 1; SELECT from_unixtime (unix_timestamp ()) AS currenttime FROM sample_07 LIMIT 1;

There are various date part functions that will extract the relevant parts from a Timestamp string.

SELECT anon_id,              from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy'))                   AS proper_date,             year (from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy')))                  AS full_year,             month (from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy')))                 AS full_month,             day (from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy')))                AS full_day,            last_day (from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy')))               AS last_day_of_month,            date_add ( (from_unixtime (UNIX_TIMESTAMP (reading_date, 'ddMMMyy'))),10)               AS added_days FROM elec_days_c ORDER BY proper_date; Conclusion

In the article, we covered some basic Hive functions and queries. We saw that running queries on distributed data is not much different from running queries in MySQL. We covered some same basic queries like inserting records, working with simple functions, and working with aggregation functions in Hive.

Key Takeaways

Hive Query Language is the language supported by Hive.

HQL makes it easy for developers to query on Big data.

HQL is similar to SQL, making it easy for developers to learn this language.

I recommend you go through these articles to get acquainted with tools for big data:

Frequently Asked Questions

Q1. What queries are used in Hive?

A. Hive supports the Hive Querying Language(HQL). HQL is very similar to SQL. It supports the usual insert, update, delete, and merge SQL statements to query data in Hive.

Q2. What are the benefits of Hive?

A. Hive is built on top of Apache Hadoop. This makes it an apt tool for analyzing Big data. It also supports various types of connectors, making it easier for developers to query Hive data using different programming languages.

Q3. What is the difference between Hive and MapReduce?

A. Hive is a data warehousing system that provides SQL-like querying language called HiveQL, while MapReduce is a programming model and software framework used for processing large datasets in a distributed computing environment. Hive also provides a schema for data stored in Hadoop Distributed File System (HDFS), making it easier to manage and analyze large datasets.

Related

X Analytics: How Industries Will Continue To Benefit From Data Analytics?

While predictive analytics, descriptive analytics continue to disrupt industries, companies will soon adopt X- analytics practices

Organizations of each technical field and non-natives are producing more data than ever before. In today’s data-driven world, if the data is analyzed using right tools, the generated insights can help fact-based decision-making. This means analytics is something that every manager, business leader or in fact anyone who works in data-based industry should be aware of. And by industry, analytics is not limited to IT operations but is also leveraged in healthcare, sports, and other relevant sectors. Not only that, they must also familiarize themselves with upcoming data analytics tools and trends, e.g. X analytics. In the Gartner Top 10 Trends in Data and Analytics for 2023, the analyst firm mentions about ‘X analytics’ which is primed to gain more traction in coming years. Gartner defines this as an umbrella term, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc. Soon, global leaders will be employing X analytics to solve toughest challenges of the world, including climate change, disease prevention and wildlife protection. Gartner also mentions that when combined with AI and other techniques such as graph analytics (another top trend), X analytics will play a key role in identifying, predicting and planning for natural disasters and other business crises and opportunities in the future.  

Existing Data Analytics Tools

Last year, the pandemic acted as enabler and catalyst for organizations to utilize

The Future: X analytics

Organizations of each technical field and non-natives are producing more data than ever before. In today’s data-driven world, if the data is analyzed using right tools, the generated insights can help fact-based decision-making. This means analytics is something that every manager, business leader or in fact anyone who works in data-based industry should be aware of. And by industry, analytics is not limited to IT operations but is also leveraged in healthcare, sports, and other relevant sectors. Not only that, they must also familiarize themselves with upcoming data analytics tools and trends, e.g. X analytics. In the Gartner Top 10 Trends in Data and Analytics for 2023, the analyst firm mentions about ‘X analytics’ which is primed to gain more traction in coming years. Gartner defines this as an umbrella term, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc. Soon, global leaders will be employing X analytics to solve toughest challenges of the world, including climate change, disease prevention and wildlife protection. Gartner also mentions that when combined with AI and other techniques such as graph analytics (another top trend), X analytics will play a key role in identifying, predicting and planning for natural disasters and other business crises and opportunities in the chúng tôi year, the pandemic acted as enabler and catalyst for organizations to utilize data analytics capabilities to a plethora of reasons. For business brands trying to learn more about their customers and find ways to be more efficient in offering personalized programmes, analytics processes like sentiment analytics, predictive analytics came to rescue to help brands understand the changing demands and expectations. In other sectors too, analytics enabled forecasting demand, identifying potential supply-chain disruptions, targeting support services to at-risk workers, and determining the effectiveness of crisis intervention strategies and more. Even epidemiologists and healthcare officials used analytics to understand the spread of coronavirus, identify emerging hotspots, vulnerable populations and trace infection waves, among many others. Location analytics helped with contact tracing and contextualize specific figures pertaining to sales, logistics and supply chain, and measure location-wise success rates of marketing campaigns. New data analytics patterns also came into light. E.g. some enterprises pivoted towards descriptive analytics over predictive analytics, as the former offers better data insights about the present and recent past. It was also a good year for cloud analytics, as more and more organizations switched to cloud amid pandemic emergencies. Diagnostic analytics become more popular in retail and healthcare industries, as it provided in-depth insights into a particular problem countered by the stakeholders. Further, video analytics went mainstream since it offers real-time update about the subjects during mass surveillance programs, which were instituted to prevent coronavirus infections, track shipment at logistics center and chúng tôi wonder that the market demand and use cases for analytics will rise in coming years, while new analysis tools will emerge. X analytics that encompasses varied formats of data types, will allow organizations to extract value from all data types, compare the old dataset against the new ones to understand how behavior has changed, what patterns have remained, and how to capitalize on these shifts. When used in conjunction with other analytics methodologies like predictive analytics, descriptive analytics, X analytics will reap enormous benefits. By mining insights from all data types, X analytics will augment the capability to extract maximal value based information from all touchpoints. However, before that, data professionals must develop models that will enable all data types to talk to each other and come together to provide the end-to-end analysis.

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