In the video below, we take a closer look at Spring Boot With Spring Data JPA [Book] | Spring Boot CRUD Example with RESTful APIs and JPA. Let's get started!
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Refactoring is the process of modifying a software system without changing its desirable behavior. It was necessary to have an application integrated with the relational database using the Spring JDBC Template in the first parts. The Spring JDBC Template is a powerful tool that facilitates productivity. However, there is a way to simplify the code even further with Spring Data JPA. The purpose of this post is to refactor the project to use Spring Data JPA.
Spring Data JPA, part of the larger Spring Data family, makes it easy to implement JPA-based repositories easily. This module deals with enhanced support for JPA-based data access layers. It makes it easier to build Spring-powered applications that use data access technologies.
A safe code refactoring requires the use of tests to ensure that the compartment is not changed. The use of tests, fortunately, is adopted as a minimum standard, including several methodologies such as TDD that preach the creation of tests at the beginning of the development process.
#java #tutorial #spring #spring data #java tutorial #spring tutorial #spring data jpa
CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.
The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.
Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-
This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.
#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data
A quick introduction to Spring data Projections!
Spring Data supports custom queries in Repositories where developers just need to follow a set way of writing repository methods for the functionality they are looking for. Just defining the method in the interface does the job, for most types of searches at least. Developers pick the functionality and the fields to search by, create the method and pass in the required parameters and the method does what it is supposed to do.
This is pretty straightforward and easy. But say you don’t (for whatever reason) want the whole entity or object returned as a part of your query. Say you only want a subset of fields from the original entity: Spring data projections to the rescue!
Spring supports different types of projections: interface-based, class-based, and dynamic projections. For the sake of this article, I am going to stick to interface-based projections. The Spring Data documentation has excellent information on the other types!
Before we jump in, I am using the following versions of tools and languages:
On that note, let us talk about how to use spring data projections step by step, using books as the resource we want to manage:
#jpa #spring-data #spring-boot #programming #kotlin #spring data projections