#Advanced Strength of Solr

[inextures]

How Solr Uses Advanced Search to Strengthen Organizations?

Solr’s advanced search technology allows for better precision and customization, leading to stronger and more efficient organizations.

We often sense information overload in the digital era, therefore organizations are continuously looking for solutions to efficiently search for and recover essential data. This is where the Solr search engine, which is based on Apache Lucene, comes in, with powerful search tools that have the ability to boost organizations in a variety of ways.

Organizations can boost client satisfaction and engagement by enhancing the importance of their search results with Solr’s advanced search features. Users may discover the information they need quickly and precisely because of Solr’s interactive search, smart search, and spell-checking capabilities. This improves not only the user experience but also the organization’s fruitfulness and productiveness.

Solr can manage massive amounts of data and allow distributed searching and indexing while providing a lightning-fast search experience.

The combination of Solr and machine learning techniques and recommendation algorithms enable personalized search outcomes. Organizations can utilize Solr’s advanced search features to give personalized search results, proposals, and suggestions by analyzing user behavior and interests. This level of personalization boosts user participation, sales, and client retention.

How does Solr manage queries?

Solr transforms the needed data into a structured representation as part of the indexing process. This entails parsing the data, extracting essential information, and categorizing it. If you’re indexing a group of documents, Solr can pull the title, author, content, and other metadata from each document and store it in distinct fields. Solr supports a variety of data formats, including XML, JSON, CSV, and others.

How Solr’s Advanced Search Can Benefit Your Business

Apache Solr Consulting Services can provide additional benefits to businesses leveraging Solr’s advanced search capabilities. Businesses can benefit from Solr’s sophisticated search capabilities in a variety of ways, including the ability to provide strong and efficient search experiences for their users. Here are some examples of how Solr’s advanced search functions might help your business:

Algorithms for ranking relevance: Solr has a number of relevance ranking algorithms that may be modified and fine-tuned to meet your unique business requirements. To assess the relevancy of search results, you can apply varying weights to various factors such as keyword matching, field enhancements, and proximity. You may ensure that the most relevant and significant results appear at the top of the search results list by customizing these algorithms.

Filtering and boosting: Solr allows you to boost or promote select documents or fields depending on specific criteria. Greater relevance scores can be assigned to specific attributes, such as product names, titles, or customer ratings, to guarantee they have a bigger effect on the overall ranking of search results. You can also use filters to narrow down search results based on specific criteria, enhancing relevancy and accuracy even further.

Sorting and relevance evaluation: Solr allows you to arrange search results based on criteria such as relevancy, date, or any other field value. You can set the sorting order to guarantee that the most relevant or recent results appear at the top of the search results list. Solr computes relevance scores based on parameters such as keyword frequency, field boosts, and other relevance ranking methods, allowing you to fine-tune search result ranking.

Better user experience: Faceted search allows users to explore and refine search results in a natural and dynamic manner. Users can rapidly drill down into certain features and locate the most relevant information by showing relevant facets or categories connected to the search results. This improves the overall user experience by streamlining the search process and shortening the time it takes to find desired results.

Facet counts that change dynamically: Solr can dynamically generate facet counts, displaying the number of matching documents for each facet value in real-time. This guarantees that the facet values appropriately represent the possibilities that are currently accessible depending on the search results. Users may see how many results are connected with each aspect value, allowing them to make more educated filtering decisions.

Conclusion

The capacity to process vast amounts of data and give real-time search updates guarantees that organizations can keep up with ever-changing data landscapes and present users with up-to-date information.

Furthermore, Solr’s connection with external systems and support for multilingual search enables organisations to search and index data from multiple sources smoothly, eliminating language barriers and offering a uniform search experience across disparate datasets.

The advanced search features of Solr serve as a foundation for organisations, allowing them to strengthen their operations, drive innovation, and gain meaningful insights from their data, eventually leading to better efficiency and success in today’s data-driven world.

Originally published by: How Solr Uses Advanced Search to Strengthen Organizations?

[afiliatevasu]

What is Solr – Comparing Apache Solr vs. Elasticsearch

In the world of search engines and data retrieval systems, Apache Solr and Elasticsearch are two prominent contenders, each with its strengths and unique capabilities. These open-source, distributed search platforms play a crucial role in empowering organizations to harness the power of big data and deliver relevant search results efficiently. In this blog, we will delve into the fundamentals of Solr and Elasticsearch, highlighting their key features and comparing their functionalities. Whether you're a developer, data analyst, or IT professional, understanding the differences between Solr and Elasticsearch will help you make informed decisions to meet your specific search and data management needs.

Overview of Apache Solr

Apache Solr is a search platform built on top of the Apache Lucene library, known for its robust indexing and full-text search capabilities. It is written in Java and designed to handle large-scale search and data retrieval tasks. Solr follows a RESTful API approach, making it easy to integrate with different programming languages and frameworks. It offers a rich set of features, including faceted search, hit highlighting, spell checking, and geospatial search, making it a versatile solution for various use cases.

Overview of Elasticsearch

Elasticsearch, also based on Apache Lucene, is a distributed search engine that stands out for its real-time data indexing and analytics capabilities. It is known for its scalability and speed, making it an ideal choice for applications that require near-instantaneous search results. Elasticsearch provides a simple RESTful API, enabling developers to perform complex searches effortlessly. Moreover, it offers support for data visualization through its integration with Kibana, making it a popular choice for log analysis, application monitoring, and other data-driven use cases.

Comparing Solr and Elasticsearch

Data Handling and Indexing

Both Solr and Elasticsearch are proficient at handling large volumes of data and offer excellent indexing capabilities. Solr uses XML and JSON formats for data indexing, while Elasticsearch relies on JSON, which is generally considered more human-readable and easier to work with. Elasticsearch's dynamic mapping feature allows it to automatically infer data types during indexing, streamlining the process further.

Querying and Searching

Both platforms support complex search queries, but Elasticsearch is often regarded as more developer-friendly due to its clean and straightforward API. Elasticsearch's support for nested queries and aggregations simplifies the process of retrieving and analyzing data. On the other hand, Solr provides a range of query parsers, allowing developers to choose between traditional and advanced syntax options based on their preference and familiarity.

Scalability and Performance

Elasticsearch is designed with scalability in mind from the ground up, making it relatively easier to scale horizontally by adding more nodes to the cluster. It excels in real-time search and analytics scenarios, making it a top choice for applications with dynamic data streams. Solr, while also scalable, may require more effort for horizontal scaling compared to Elasticsearch.

Community and Ecosystem

Both Solr and Elasticsearch boast active and vibrant open-source communities. Solr has been around longer and, therefore, has a more extensive user base and established ecosystem. Elasticsearch, however, has gained significant momentum over the years, supported by the Elastic Stack, which includes Kibana for data visualization and Beats for data shipping.

Document-Based vs. Schema-Free

Solr follows a document-based approach, where data is organized into fields and requires a predefined schema. While this provides better control over data, it may become restrictive when dealing with dynamic or constantly evolving data structures. Elasticsearch, being schema-free, allows for more flexible data handling, making it more suitable for projects with varying data structures.

Conclusion

In summary, Apache Solr and Elasticsearch are both powerful search platforms, each excelling in specific scenarios. Solr's robustness and established ecosystem make it a reliable choice for traditional search applications, while Elasticsearch's real-time capabilities and seamless integration with the Elastic Stack are perfect for modern data-driven projects. Choosing between the two depends on your specific requirements, data complexity, and preferred development style. Regardless of your decision, both Solr and Elasticsearch can supercharge your search and analytics endeavors, bringing efficiency and relevance to your data retrieval processes.

Whether you opt for Solr, Elasticsearch, or a combination of both, the future of search and data exploration remains bright, with technology continually evolving to meet the needs of next-generation applications.

[felipeandletizia]

May 4, 2020: King Felipe and Queen Letizia held a videoconference with representatives of the field of the Digital Agenda and Artificial Intelligence. They value the advance of the Digitization and the use of Artificial Intelligence in the period of fight against the pandemic.

In the videoconference, the Secretary of State for Digitization and Artificial Intelligence, Carme Artigas; The President of the Spanish Association for Artificial Intelligence, Amparo Alonso, and the expert in ethics and professor of quantum physics, José Ignacio Latorre, have spoken to them about digitization, artificial intelligence and the response to COVID19.

As indicated to Don Felipe and Doña Letizia, Spain is, without a doubt, a country with strengths in this regard and in which the digitization and use of AI are progressing substantially in this period to combat COVID19.

The Secretary of State for Digitization and Artificial Intelligence has shared with Their Majesties the Kings the main axes of the future National Strategy for Artificial Intelligence and has reviewed the digital solutions developed by the government of Spain, made available to the autonomous communities to contribute to the management of the health emergency: among them, the Covid-19 Assistance self-diagnosis application that allows health authorities to decongest the telephone numbers and indicate guidelines to the public; Conversational Assistant Hispabot-Covid19 (which uses artificial intelligence and natural language to respond to citizens' concerns about COVID-19 with official, accurate and updated information, through instant messaging services such as WhatsApp or Telegram, with 193,000 queries answered); the DataCOVID mobility study based on anonymous and aggregated data from mobile devices provided by the country's three main operators (the study data shows that, generally, since the state of alarm was decreed, 85% of the citizens have not moved from their area of ​​residence to other places); or the official technological resources website www.Covid19.gob.es, a space in which information, news and various digital resources related to the COVID-19 crisis are collected in a unified way to make them available to all citizens

In addition to these main axes, the Secretary of State is part of the Group of the European Commission where Member States have been meeting for several weeks to agree on a common response regarding contact tracking applications; maintains fluid communication and collaboration with autonomous communities and cities to respond in a coordinated manner from the technological side to the pandemic and has signed the G-20 Joint Declaration to promote digital solutions to COVID-19, which defines the main lines of action in the development of digital innovations against the virus at a global level.

For its part, the Spanish Association for Artificial Intelligence (AEPIA), as its president told His Majesties the Kings, has carried out a search engine (SOLR index) with all the documents of the corpus CORD-19 (COVID-19 Open Research Dataset - a Source of more than 57,000 articles on COVID-19, SARS-CoV-2, and other related coronaviruses in which, for example, you can search for scientific articles that mention chloroquine, use drugs that combine penicillin and / or beta-lactamase inhibitors or describe antiviral treatments with Interferon-; the intelligent system WASPSS (Wise Antimicrobial Stewardship Program Support System), designed for hospital professionals working in the rational use and optimization of antibiotics (PROA) programs , with the final objective of facilitating the management of antibiotic treatments - their adaptation is currently being evaluated for their generalization in 11 public hospitals from all over Spain-; the open and global initiative #innovacionfrentealvirus, which supports the creation of a technological, social and innovative community of international impact to help as far as possible to mitigate the effects of COVID-19 in which Universities, Research Groups, Spin would be integrated OFFs, Innovators, Startups, Corporations, Investors, Innovative SMEs, Public Institutions, Media, etc…; and a clinical decision support system for infection surveillance / Clinical Decision Support System for Infection Surveillance. The objective of the project is to develop a clinical decision support system. It focuses on models to analyze the spread of infections, on the prediction of the appearance of multi-resistance, on interpretable models for the detection of risk factors, etc.

Finally, José Ignacio Latorre has explained the importance of an ethical reflection on the era of artificial intelligence and how this vision must incorporate our elders, who are the great forgotten of AI. Latorre, author of the book "Ethics for machines", is professor of Theoretical Physics at the University of Barcelona. He is currently the director of the new Quantum Research Center at the Technology Innovation Institute in Abu Dhabi, whose main objective is to build a quantum computer. In July he will become the director of the Center for Quantum Technologies in Singapore. His lines of research cover elementary particles, quantum computing, and artificial intelligence. In her dissemination work, she defends the need to establish ethical criteria in the use of advanced technologies.

[raj89100]

What is solr developer task & its importance?

Apache Solr is a popular enterprise-level search platform which is being used widely by popular websites such as Reddit, Netflix, and Instagram. The reason for the popularity of Apache Solr is its well-built text search, faceted search, real-time indexing, dynamic clustering, and easy integration. Apache Solr helps building high level search options for the websites containing high volume data. Enhance your Business with Solr If you have a website with a large number of documents, you must need good content management architecture to manage search functionality. Let it be an e-commerce portal with numerous products or website with thousand pages of lengthy content, it is hard to search for what you exactly want. Here comes integrated Solr with a content management system to help you with fast and effective document search. At Prominent Pixel, we offer comprehensive consulting services for Apache Solr for eCommerce websites, content-based websites, and internal enterprise-level content management systems.

What we do Our Solr Solution & Services Leading search solution provider for Terabyte sized, Customized, Scalable search solutions. Consulting for Solr Architectural Design Our Solr consulting services include Search application assessment, Solr strategy consulting, Solr search engine tuning, Migration, Managed services, Support and Training, and Big Data application consulting and implementation.

Custom CMS Integration Things are made easy with one click CMS integration with Solr. Hire dedicated Apache Solr developer from Prominent Pixel for the best custom CMS integration. Our developers are experts in Drupal and Solr integration which helps your business grow.

Solr Installation & Configuration Our dedicated Apache Solr developers will install Solr on the right platform by choosing the Java Runtime Environment with the right version. After the installation process, our developers will configure on module level. Disaster Recovery and Replication Hire our Apache Solr developers to get help in indexing corruption issues, malicious administrative actions, accidental data entry or subtractions. Also, we ensure redundancy for your data through replication.

Solr Plugin Development The Solr framework helps easy plugin development that extends the capability of the software to the maximum. Hire dedicated Apache Solr developers from Prominent Pixel to get custom Solr plugins.

Solr Performance Tuning, Load Balancing and Load Testing Our dedicated Apache Solr developers will help you maximize the Solr performance by conducting performance tuning, load balancing, and load testing. Choose and hire our Solr developer right now to accomplish your goals. What we do Our Technical Skills & Key Strengths Our core expertise is in Solr Architectural Design, Custom plugin development for Solr & Solr custom Solutions. Development Skills Our Solr Developers have years of experience and expertise in developing Solr plugins and websites. Hire our dedicated Apache Solr development programmers to get your work done perfectly. Tools Our Apache Solr developers use core-specific tools as well as collection-specific tools depending on the requirement of your project. Concept Comprehensive administrative interface, easy monitoring, Near real-time indexing, Extensible plugin architecture, High volume traffic, and more. Why Hire Solr Developers From Prominent Pixel? Prominent Pixel has a team of expert and experienced Apache Solr developers who have worked on several Solr projects to date. Our developers’ will power up your enterprise search with flexible features of Solr. Our Solr services are specially designed for eCommerce websites, content-based websites and enterprise-level websites. Also, our dedicated Apache Solr developers create new websites with solar integrated content architecture. Why us? Key Benefits Apache Solr is preferred for its powerful search technology which has rich features that reduces the overall search time. The easy integration with Content Management Systems like Drupal, Wordpress, and e-commerce platforms like Magento, OpenCart, WooCommerce, and others is the reason why you should choose Apache Solr development. Powerful Extensions Solr comes with optional plugins with indexing rich content, language detection, search results clustering, and much more. Advanced Configurable Search Analysis Solr supports many languages, such as English, Chinese, Japanese, German, French etc. Also, there are many analysis tools for indexing and querying the content flexibly. Built-in Security You can secure Solr with SSL, Authentication, and Role-based authorization. Full-Text Search The advanced full-search options of Apache Solr include excellent matching abilities such as wildcards, phrases, joins, grouping and much more. Open Interfaces with Rich Standards Building an app has become much easier with Solr by using open interfaces like XML, HTTP, and more. Solr Optimization Optimized for high volume traffic and proved to the world at a large scale. Our dedicated Solr developer helps you optimize for high traffic that you have ever expected. Our Hiring Process We offer personalized and flexible pricing packages to our clients. Hire full time, part time or hourly basis as per your needs.

[restattechtk-blog]

Light a Fire under Cassandra with Apache Ignite

Apache Cassandra is a well known database for a few reasons. The open source, appropriated, NoSQL database has no single purpose of disappointment, so it's appropriate for high-accessibility applications. It bolsters multi-datacenter replication, enabling associations to accomplish more prominent strength by, for instance, putting away information over numerous Amazon Web Services accessibility zones. It additionally offers huge and straight adaptability, so any number of hubs can without much of a stretch be added to any Cassandra bunch in any datacenter. Consequently, organizations, for example, Netflix, eBay, Expedia, and a few others have been utilizing Cassandra for key parts of their organizations for a long time.

After some time, be that as it may, as business necessities advance and Cassandra arrangements scale, numerous associations get themselves compelled by some of Cassandra's constraints, which thusly confine what they can do with their information. Apache Ignite, an in-memory registering stage, furnishes these associations with another approach to get to and deal with their Cassandra foundation, enabling them to make Cassandra information accessible to new OLTP and OLAP utilize cases while conveying to a great degree elite.

[ NoSQL fight: MongoDB and Couchbase Server go nose to nose. | Keep up with intriguing issues in programming with InfoWorld's Application Development pamphlet. ]

Impediments of Cassandra

A key restriction of Cassandra is that it is plate based, not an in-memory database. This implies read execution is constantly topped by I/O particulars, at last confining application execution and constraining the capacity to achieve a worthy client encounter. Consider this examination: What can be handled on an in-memory framework in a solitary moment would take decades on a circle based framework. Notwithstanding utilizing streak drives, it would in any case take months.

While Cassandra offers quick information compose execution, accomplishing ideal read execution requires that the Cassandra information be composed to circle consecutively, so that on peruses, the plate head can check for whatever length of time that conceivable without the inertness of the head jumping from area to area. To accomplish this, the questions should be basic, with no JOINs, GROUP BYs, or collection, and the information must be demonstrated for those inquiries. Consequently, Cassandra offers no specially appointed or SQL inquiry capacity by any stretch of the imagination.

DataStax, an organization that creates and offers help for a business version of Apache Cassandra, added a capacity to associate Cassandra to Apache Spark and Apache Solr to bolster investigation. In any case, this technique gives restricted advantage since utilizing connectors is an exceptionally costly approach to get to a subset of the information. The information still must be set down consecutively or the execution will be poor since Cassandra would need to do a full table sweep, which is a scramble/assemble approach including a lot of circle inactivity.

Another possibly critical impediment of Cassandra is that it just backings inevitable consistency. Its absence of full ACID consistence implies it can't be utilized for applications that move cash or require ongoing stock data.

Subsequently of these constraints, associations needing to utilize the information they have put away in Cassandra for new business activities regularly battle with how to do as such.

Enter Apache Ignite

Apache Ignite is an in-memory registering stage that can help beat these constraints in Cassandra while staying away from the overhead expenses of the connector approach. Apache Ignite can be embedded between Apache Cassandra and a current application layer without any progressions to the Cassandra information and just negligible changes to the application. The Cassandra information is stacked into the Ignite in-memory group, and the application straightforwardly gets to the information from RAM rather than from plate, quickening execution by no less than 1,000x. Information composed by the application is composed first to the Ignite group for prompt, progressing utilization. It is then composed to plate in Cassandra for changeless stockpiling with either synchronous or nonconcurrent composes.

Apache Ignite additionally has the same compose procedure as Apache Cassandra, so it will feel commonplace to Cassandra clients. Like Cassandra, Ignite is open source and its clients advantage from a huge and dynamic group, with bolster accessible through various group sites. As an in-memory processing stage, be that as it may, Apache Ignite empowers associations to do a great deal more with their Cassandra information—and do it quicker. Here's the ticket.

More information alternatives—ANSI SQL-99 and ACID exchange ensures

Fueled by an ANSI SQL-99-consistent motor, Apache Ignite offers ACID exchange ensures for appropriated exchanges. Its In-Memory SQL Grid gives in-memory database capacities, and ODBC and JDBC APIs are incorporated. By consolidating Ignite with Apache Cassandra, any sort of OLAP or complex SQL inquiry can be composed against Cassandra information that has been stacked into Ignite. Touch off can likewise be worked in different modes from possible consistency to constant, full ACID consistence, enabling associations to utilize the information put away in Cassandra (however perused into Ignite) for a large group of new applications and activities.

No rebuilding of Cassandra information

Apache Ignite peruses from Apache Cassandra and other NoSQL databases, so moving Cassandra information into Ignite requires no information adjustment. The information diagram can likewise be moved straightforwardly into Ignite as seems to be.

More noteworthy speed for information concentrated applications

Moving the majority of the Apache Cassandra information into RAM offers the quickest conceivable execution and extraordinarily enhances question speed in light of the fact that the information is not continually being perused from and written to plate. It is likewise conceivable to utilize Apache Ignite to reserve just the dynamic part of the Cassandra information to accomplish a noteworthy speed support. Touch off's lists likewise live in memory, making it conceivable to perform ultrafast SQL questions on the Cassandra information that has been moved into Ignite.

Straightforward level and vertical scaling

Like Apache Cassandra, Apache Ignite effortlessly scales on a level plane by adding hubs to the Ignite group. The new hubs in a flash give extra memory to storing Cassandra information. Be that as it may, Ignite additionally effectively scales vertically. Touch off can use the greater part of the memory on a hub, not just the JVM memory, and articles can be characterized to live on or off pile and utilize all the memory on the machines. Along these lines, just expanding the measure of memory on every hub consequently scales the Ignite group vertically.

Expanded accessibility

Like Apache Cassandra, the distributed Apache Ignite processing stage is constantly accessible. The disappointment of a hub does not keep applications from writing to and perusing from characterized reinforcement hubs. Information redistribution is likewise programmed as an Ignite group develops. Since Ignite offers refined bunching backing, for example, recognizing and remediating split cerebrum conditions, the joined Cassandra/Ignite framework is more accessible than an independent Cassandra framework.

Less complex and speedier than Hadoop

Numerous associations that might want to make SQL questions into their Apache Cassandra information consider stacking the information into Hadoop. The drawback of this approach is that, in the wake of fathoming the ETL and information synchronizing challenges that emerge, the inquiries into Hadoop would at present be generally moderate. While consolidating Cassandra and Ignite will likewise bring about some little execution hit on account of the extra framework and storing, inquiries all things considered execute with blasting velocity, making the arrangement ideal for ongoing investigation. Furthermore, dealing with the connection amongst Ignite and Cassandra information is substantially less difficult.

Difficulties to actualizing Cassandra and Ignite

As noted above, consolidating Apache Cassandra and Apache Ignite involves costs. You normally bring about a hit in the execution—and cost and support—of having two systems (as you would with the expansion of some other arrangement). There is an equipment fetched for new ware servers and adequate RAM, and maybe a membership taken a toll for an undertaking grade and bolstered form of Apache Ignite. Encourage, actualizing and keeping up Ignite may require a few associations to enlist extra mastery. Accordingly, a cost/advantage investigation is justified to guarantee that the key advantages of any new utilize case, alongside the execution picks up, exceed the expenses.

In making this assurance, the accompanying contemplations are imperative. Initially, not at all like the past era of in-memory registering arrangements, which required cobbling together various items, Apache Ignite is a completely coordinated, simple to-send arrangement. Incorporating Ignite with Apache Cassandra is regularly an extremely clear process. Touch off slides amongst Cassandra and an application, for example, Apache Kafka or other customer, that gets to the information. Touch off incorporates a prebuilt Cassandra connector, which streamlines the procedure. The application then peruses and works out of Ignite rather than Cassandra, so it is continually getting to information from memory rather than from plate. Touch off naturally handles the peruses and works out of and into Cassandra.

Second, while many still consider in-memory figuring as restrictively costly, the cost of RAM has dropped around 30 percent for each year since the 1960s. In spite of the fact that RAM is still pound for pound more costly than SSDs, the execution advantage of using terabytes of RAM in an in-memory processing bunch, particularly for extensive scale, mission-basic applications, may make in-memory registering the most savvy approach.

At long last, Apache Ignite is a sure thing with a develop codebase. It started as a private venture in 2007, was given to the Apache Software Foundation in 2014, and graduated to a top-level venture about a year later—the second-quickest Apache venture to graduate after Apache Spark.

Apache Cassandra is a strong, demonstrated arrangement that can be an indispensable component of numerous information procedures. With Apache Ignite, Cassandra information can be made more useful.The Apache Ignite in-memory comput

[lewiskdavid90]

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A comprehensive Java course integrated with time-tested design rules, best practices, demos & industry-strength project.

38.5 hours,  – 5 coding exercises,  260 lectures 

Average rating 4.5/5 (4.5 (330 ratings) Instead of using a simple lifetime average, Udemy calculates a course’s star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.)

Course requirements:

Familiarity in using computers The 3 P’s: Patience, perseverance, and passion. Please note it is not a crash course!!

Course description:

Update on December 25th, 2016: Lambdas & Nested Classes have been added as two new sections.

Few Representative Student Reviews:

“Got a job thanks to this course!” ~ Connor Lee

“This is THE best course on Java on Udemy – Period! Dheeru is not only passionate about what he is coaching but also OBSESSIVE and covers every minute detail of the subject … Most lessons have demos which Dheeru makes sure that they do work without any glitches. He is a genius coder … Plus, he bases the course on the best practices from the book “Effective Java” which is great. You get to cover most of this book if you study this course! … Lastly, he uses an accurate and powerful English vocabulary I’m yet to see from other instructors. If you want to learn Java right from installing, configuring and all the way to mastering its advanced topics – look no further – you are at the right place => THIS – IS – IT !!!” ~ Richard Reddy

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This comprehensive project-based course has been designed with following objectives:

To help you get an in-depth understanding of both core & advanced concepts in Java To teach best practices & key design principles for writing effective Java programs To help you gain practical experience in writing professional-level code via instructor-led implementation of a project. Project is implemented in Eclipse using MVC design pattern, TDD (Test-Driven Development) principles and other best practices To help you understand the inner workings of Java platform (aka JVM) To teach how to effectively use Eclipse IDE to write, debug, and test Java programs

The motivation behind these objectives is to help you in becoming a complete & industry-ready engineer. Most Java courses focus only on teaching core fundamentals, which at the very best equip students with good basic skills to work on small-to-medium sized projects. Most of these students fall short when it comes to implementing more complex projects. To design elegant solutions for large, complex projects one needs to have a much deeper understanding of language features along with knowledge of the recommended best practices & design principles. More importantly one needs to know how to put all of these into practice. This comprehensive one-stop project-based course has been designed to equip students with these skills.

Course Specifics

This course begins by giving a birds-eye view of Java covering everything from origin of Java to installing Java and writing your first Java program. Even the most fundamental concepts like compilation & interpretation are explained.

Reviews:

“This is THE best course on Java on Udemy – Period! Dheeru is not only passionate about what he is coaching but also OBSESSIVE and covers every minute detail of the subject. At the beginning of every chapter, he comes up with a brief Agenda and digs deeper into each topic on the agenda before diving into the actual course. Most lessons have demos which Dheeru makes sure that they do work without any glitches. He is a genius coder. At the end of each chapter, there is a challenging Quiz and if it is a coding quiz, it is coupled with a JUNIT code block that accurately and automatically evaluates your solution/code for accuracy – he puts technology to work! He concludes each chapter with a Summary of topics covered and followed by an exhaustive Conclusion digging deeper into the topics covered which should NOT be missed – it is reinforcing and revising what we learnt. The conventions and the format of the course he adopted in this course show his coaching experience and also mastery over the subject he is teaching. The initial chapter on how to use the Udemy video player and its features is cool as well. Plus, he bases the course on the best practices from the book “Effective Java” which is great. You get to cover most of this book if you study this course! Lastly, he uses an accurate and powerful English vocabulary I’m yet to see from other instructors. If you want to learn Java right from installing, configuring and all the way to mastering its advanced topics – look no further – you are at the right place => THIS – IS – IT !!!” (Richard Reddy)

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  About Instructor:

Dheeru Mundluru

A passionate software engineer and instructor, Dheeru has 10 years of experience developing innovative software for start-ups in silicon valley and elsewhere. He holds a Ph.D. in Computer Science from University of Louisiana at Lafayette (USA). His expertise includes developing complex Web data integration & mining software with Java as the main programming language. Coming from the start-up world, he also has extensive end-to-end experience in developing Web applications using frameworks/tools such as Spring, Hibernate, Solr, MySql, etc. Dheeru is passionate about developing products that are easy-to-use, intelligent, and well-architected. Writing well-crafted code that follows the best design practices is of utmost importance to him. He brings the same level of passion and completeness to his teaching. Every concept is covered at a very in-depth level clearly explaining the motivation behind their introduction. He strongly believes in “learn by involving” teaching principle and thus his courses involve tons of live demos, an industry standard project, and several quizzes. Prior to his current gig at his start-up SemanticSquare, Dheeru worked for around 5 years as a Principal Engineer for NimbleCommerce, an e-commerce start-up in Santa Clara, California. Before NimbleCommerce, he worked as a Research Scientist at Local Corporation, a local search company in Irvine, California. He also published and presented half-a-dozen research papers at top conferences and workshops such as International Conference on Data Mining (ICDM) and Geographic Information Retrieval (GIR). During his graduate-school days, as a teaching assistant for Search & Data Mining courses, he designed course assignments and often gave guest lectures on Web data mining.

Instructor Other Courses:

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