28 items found: Search results for "spring data" in all categories x
October 23, 2014 | Cassandra
Spring Data Cassandra (SDC) is a community project under the Spring Data (SD) umbrella that provides convenient and familiar APIs to work with Apache Cassandra.
July 4, 2013 | Software Consultancy
In which situations Spring Data Hadoop (SDH) can add value, and in which situations would it be a poor choice? This article follows on from an objective summary of the features of SDH.
June 30, 2013 | Software Consultancy
Spring Data Hadoop (SDH) is a Spring offshoot project that allows the invocation and configuration of Hadoop tasks within a Spring application context. It offers support for Hadoop jobs, HBase, Pig, Hive, Cascading and additionally JSR-223 scripting for job preparation and tidy-up.
It is most suited for use in organisations with existing Spring applications or investment in Spring expertise. Some SDH features replicate functionality of tools in the Hadoop ecosystem that DevOps engineers who maintain a Hadoop cluster will be more familiar with.
March 10, 2022 | Data Engineering, Open Source
In this lunch & learn session, Ebru Cucen and Alberto Faedda explore the historical background of GraphQL with case examples and a demo of how it can be used.
December 1, 2015 | Software Consultancy
This post introduce some of the basic features of Hazelcast, some of its limitations, how to embed it in a Spring Boot application and write integration testings. This post is intended to be the first of a series about Hazelcast and its integration with Spring (Boot). Let’s start from the basics.
November 1, 2015 | Microservices
To use or not to use hypermedia (HATEOAS) in a REST API, to attain the Level 3 of the famous Richardson Maturity Model. This is one of the most discussed subjects about API design.
The many objections make sense (“Why I hate HATEOAS“, “More objections to HATEOAS“…). The goal of having fully dynamic, auto-discovering clients is still unrealistic (…waiting for AI client libraries).
However, there are good examples of successful HATEOAS API. Among them, PayPal.
February 24, 2014 | Cloud Native, Microservices
Last year some of us attended the London Spring eXchange where we encountered a new and interesting tool that Pivotal was working on: Spring Boot. Since then we had the opportunity to see what it’s capable of in a live project and we were deeply impressed.
News | September 1, 2011
December 21, 2014 | Publications
Neo4j in Action is a comprehensive guide to designing, implementing, and querying graph data using Neo4j. Using hands-on examples, you’ll learn to model graph domains naturally with Neo4j graph structures. The book explores the full power of native Java APIs for graph data manipulation and querying.
April 18, 2018 | Microservices
Quite a few of the anti-patterns we observe today on microservices projects are strongly related to how people approach the problem. Given their nature, these anti-patterns tend to be deeply ingrained and self-sustaining. Addressing them starts with increased awareness and by changing ways of approaching the problem, rather than by the introduction of yet another technical tool or framework.
March 7, 2017 | Data Analysis, GCP
Google has recently made its internal Spanner database available to the wider public, as a hosted solution on Google Cloud. This is a distributed relational/transactional database used inside for various Google projects (including F1, the advertising backend), promising high throughput, low latency and 99.999% availability. As such it is an interesting alternative to many open source or other hosted solutions. This whitepaper gives a good theoretical introduction into Spanner.
February 28, 2017 | Software Consultancy
As Kotlin’s 1.1 release draws closer, I’ve been looking at some of the new language features it supports. Type aliases may seem like a relatively minor feature next to coroutines, but as I will show in this blog post, they can open up a new programming idiom, particularly when combined with extension functions.
February 13, 2017 | Data Engineering
One of the stated intentions behind the design of Java 8’s Streams API was to take better advantage of the multi-core processing power of modern computers. Operations that could be performed on a single, linear stream of values could also be run in parallel by splitting that stream into multiple sub-streams, and combining the results from processing each sub-stream as they became available.
January 26, 2017 | Data Engineering
Suppose you are given the task of writing code that fulfils the following contract:
This blog is written exclusively by the OpenCredo team. We do not accept external contributions.
October 13, 2016 | Data Analysis
In Lisp, you don’t just write your program down toward the language, you also build the language up toward your program. As you’re writing a program you may think “I wish Lisp had such-and-such an operator.” So you go and write it. Afterward you realize that using the new operator would simplify the design of another part of the program, and so on. Language and program evolve together…In the end your program will look as if the language had been designed for it. And when language and program fit one another well, you end up with code which is clear, small, and efficient – Paul Graham, Programming Bottom-Up
September 21, 2016 | DevOps
Sometimes, it can be difficult to write automated tests for parts of your application due to complexities introduced by an external dependency. It may be flaky or have some sort of rate limiting, or require sensitive information which we don’t want to expose outside of our production environment. To get around this, teams might take the approach of manually stubbing the service or using mocks – but the former is tedious and error prone, whereas the latter doesn’t test collaboration at all.
March 17, 2016 | Software Consultancy
In order to be able to regularly release an application, your automated tests must be set up to give you fast and reliable feedback loop. If bugs are only found during a long and expensive multi-service or end-to-end test run, it can be a hinderance to fast delivery. Unfortunately I have often seen this problem in development environments: a huge suite of clunky, flaky and slow end-to-end tests which test the full functionality of the application as opposed to being more lightweight and reflecting basic user journeys. This produces the “ice cream cone” anti-pattern of test coverage, where unit tests aren’t providing the kind of coverage and feedback they need to.
October 31, 2015 | Microservices
Over the past few weeks I’ve been writing an OpenCredo blog series on the topic of “Building a Microservice Development Ecosystem”, but my JavaOne talk of the same title crept up on me before I managed to finish the remaining posts. I’m still planning to finish the full blog series, but in the meantime I thought it would be beneficial to share the video and slides associated with the talk, alongside some of my related thinking. I’ve been fortunate to work on several interesting microservice projects at OpenCredo, and we’re always keen to share our knowledge or offer advice, and so please do get in touch if we can help you or your organisation.
September 20, 2015 | Microservices
Over the past five years I have worked within several projects that used a ‘microservice’-based architecture, and one constant issue I have encountered is the absence of standardised patterns for local development and ‘off the shelf’ development tooling that support this. When working with monoliths we have become quite adept at streamlining the development, build, test and deploy cycles. Development tooling to help with these processes is also readily available (and often integrated with our IDEs). For example, many platforms provide ‘hot reloading’ for viewing the effects of code changes in near-real time, automated execution of tests, regular local feedback from continuous integration servers, and tooling to enable the creation of a local environment that mimics the production stack.
August 12, 2015 | Microservices
Over the last few months one of my main projects at OpenCredo has involved creating various microservices which are interacted with via REST. We’ve been working with a relatively rich domain model, which in turn has presented a lot of challenges in how to design our various resources. This blog post aims to summarise various techniques and practices which I’ve found helpful in overcoming these challenges.
July 14, 2015 | Software Consultancy
Building simple proxies
In the previous post I introduced Java dynamic proxies, and sketched out a way they could be used in testing to simplify the generation of custom Hamcrest matchers. In this post, I’m going to dive into some techniques for implementing proxies in Java 8. We’ll start with a simple case, and build up towards something more complex and full-featured.
November 4, 2014 | Software Consultancy
When starting a project, teams often spend their time re-inventing the ‘automated testing wheel’. While every project has it’s own challenges and every team it’s own needs, many things exist as common requirements of a flexible test automation framework.
This post introduces an effective Java test framework that can be used to quickly get started with test automation on a Java project.
February 19, 2013 | Software Consultancy
This blog post continues on from Part 1 which discussed types of tests and how to create robust tests. Part 2 will examine techniques to help whip a test suite in to shape and resolve common issues that slow everything down. The approaches in this post will focus on spring based applications, but the concepts can be applied to other frameworks too.
December 18, 2012 | Software Consultancy
The first thing most people think of when they start a project with the good intentions of test driven development is: write a test first. That’s great, and something I would fully encourage. However, diving in to writing tests without forethought, especially on large projects with a lot of developers can lead to new problems that TDD is not going to solve. With some upfront thinking (but not big upfront design!) a large team can avoid problems later down the line by considering some important and desirable traits of a large and rapidly changing test suite.
February 8, 2012 | Data Analysis, Data Engineering
Most of the important players in this space are large IT corporations like Oracle and IBM with their commercial (read expensive) offerings.
While most of CEP products offer some great features, it’s license model and close code policy doesn’t allow developers to play with them on pet projects, which would drive adoption and usage of CEP in every day programming.
