Learn how to combine two or more streams of data together in real-time with a streaming database, run SQL queries and create a materialized view.

Learn how to avoid traps with Java Optional and how to use it efficiently by taking the best of this API to make a readable code.

Last week I wrote a brief introduction to Kristof Degrave's ongoing, multi-stage IndexedDB tutorial. Judging by the number of reads, it looks like quite a few of you are interested in learning more about HTML5's IndexedDB. I'm following Kristof's tutorial anyway, so I might as well keep posting about it here. Today Kristof has posted his next IndexedDB tutorial -- Transactions -- and here's where IndexedDB begins to get exciting, where the work of creation and definition begins to pay off. We're preparing for actual data retrieval and manipulation, so we'll be creating a READ_WRITE transaction. At this point, if you're trying to understand IndexedDB formally as well as use it pragmatically, you might want to get more comfortable with W3C's conceptual treatment of transactions along with the formal object description, and maybe the IDBTransaction interface too. (For me, it especially helps to understand emerging tech like HTML5 a little more abstractly, just in case the standard takes a different turn than previously expected.) If you prefer learning by doing, here's how Kristof explains transactions: Today, I’ll handle the transaction subject. As said in previous posts, every request made to the database needs to be done in a transaction. So for every read or write request we need to create a new transaction. There for we need a database connection and 2 argument that we will pass to the transaction method. The post is, like his previous tutorials, quite straightforward -- painlessly showing you how to use what is potentially one of the most powerful features of HTML5. Take a look, create an IndexedDB transaction, and get ready to retrieve and manipulate data.

Serious local database support is probably one of the coolest new features of Windows Phone 7.1(5). For the Windows Phone developer, it's not hard to create a local database, or add some columns, indexes or tables. But if you're using a SQL CE database then you are, after all, developing for a phone. And one of phones' most exciting powers isn't their hard drives -- it's their cameras. And it turns out that Mango makes storing camera photos -- or any image data for that matter -- pretty easy. To see how easy, look at this HowTo from Anton Swanevelder, posted a few days ago on his blog. Anton breaks SQL CE image-storage into three steps (the CRU in CRUD), and every step takes less than 20 lines. For example, you can create a column to store image data like this: [Column] public byte[] ItemImage { get { return _ItemImage; } set { if (_ItemImage != value) { _ItemImage = value; NotifyPropertyChanging("ItemImage"); NotifyPropertyChanged("ItemImage"); } } } The other two steps are more interesting (converting a camera stream to a storable byte array, then converting the byte array to a bitmap markup-able in XAML), but no more difficult. Read the full post for the full implementation.

According to Philip Rathle on The New Stack, graph databases can be used for more than just finding football stadiums. In fact, they can help with some pretty interesting problems: breaking up organized crime, for example. The example Rathle relies on for this article isn't the Sopranos-style organized crime you might be picturing, but rings of bank and credit card fraudsters. These are perpetrators of "first-party fraud," defined by Rathle as people who "...apply for credit cards, loans, overdrafts, and unsecured banking credit lines with no intention of paying any of them back." This type of fraud is a major problem for financial institutions, largely because of the way fraud rings mirror the strengths of graph databases: a small number of real addresses and fake phone numbers can be tied together in different combinations to create a vast web of dummy accounts attached to fake identities. This structure of fraud is hard to detect, Rathle says: ...traditional methods of fraud detection are either not geared to look for the right thing: in this case, the rings created by shared identifiers. Standard instruments—such as a deviation from normal purchasing patterns—use discrete data and not connections. Discrete methods are useful for catching fraudsters acting alone, but they fall short in their ability to detect rings. And particularly using relational databases: Uncovering rings with traditional relational database technologies requires . . . a set of tables and columns and then carrying out a series of complex joins and self-joins. Such queries are incredibly complex to build and expensive to run. Scaling them in a way that supports real-time access poses significant technical challenges, with performance becoming exponentially worse not only as the size of the ring increases but also as the total data set grows. This is where graph databases come in uniquely handy. Rathle points to languages such as Cypher as providing a semantic that lends itself to navigating these types of relationships, and it is fairly clear, as Rathle demonstrates with a visual, how graph relationships can pinpoint rings. Take a look at Rathle's full article for more details on how graph databases can be used to traverse complex relationships and detect fraud rings.

Explore this fun experiment using DuckDB to parse CockroachDB Change Data Capture output and query CockroachDB with DuckDB.

Learn how cloud-native JuiceFS empowers quantitative hedge funds to enhance AI training and achieve elastic throughput in the cloud.

This article explores NestJS, Mongo, and Typegoose and provides an example of how you can use MongoDB in your NestJS application in a headache-free way.

Translating website and app content is challenging. Streamlining workflows and automating translation tasks improve communication and boost productivity.

IoT needs speed, reliability, and energy efficiency that isn’t guaranteed in a desktop environment. Let's look at how to choose the right real-time operating system.

In this blog, I would like to share a few best practices for creating High Available (HA) Applications in Mule 4 from an infrastructure perspective.

In this blog, we will be looking at 10 AWS EKS best practices that will help you configure, deploy, use, and manage the Kubernetes Cluster on AWS.

Several of us might be familiar with the clear () API in the Java collections framework. In this post, let’s discuss what is the purpose of this clear() API?

This PostgreSQL JSONB Cheatsheet presents a complete guide to PostgreSQL JSONB functions with examples and some demo code.

Looking for a more streamlined and intuitive way of querying databases, similar to the elegant LINQ constructs in C#? Check out the open-source library JPAStreamer.

In this article, we will delve into the technical functionality of Hyperion Essbase and explore how it enables businesses to unlock valuable insights.

Where to start when it comes to extracting and integrating enterprise applications data for data insights and being a truly data-driven enterprise.

Open-source software and cloud-native infrastructure are inextricably linked and can play a key role in helping to manage security.

This article discusses the importance of detecting threatening IP addresses in various forms and provides two API solutions to help detect those threats.

In this article, the reader will learn how to perform a failover with a headless Aurora Global Database and its outcomes.