First, there were DBMSs and data warehouses. Then came data lakes and event stream processing
platforms. Now, the most advanced data solutions are Unified Real-Time Data Platforms. But what are
they?
Unified Real-Time Data Platforms simplify and optimize data architectures by combining
transactional, stream, and analytical processing across data silos into a single “unified” platform. These…
The GridGain Systems In-Memory Computing Blog
Every industry is experiencing massive increases in the volume of data, the number of queries, and the complexity of requests. At the same time, requirements for low latency are also increasing to keep up with the speed of business. This trend has been apparent in capital markets perhaps longer than in other industries due to intense competition and a willingness to be early adopters of…
GridGain recently published a micro-learning unit on GridGain University that delves into the different data loading strategies for Apache Ignite. These strategies include initial/regular batch load from files, database loading, real-time streaming, and ETL (Extract Transform & Load).
Here is an overview of some of the key strategies. Watch the full 9-minute video here. Initial / Regular…
In-memory computing can provide tremendous benefits for the 5G ecosystem. We’ve seen the marketing for the fifth-generation mobile networks. The benefits of 5G for end-users are easy to understand. Speeds faster than your home broadband and latencies only a little slower promise to be game-changers for consumers, enhancing existing applications and opening open entirely new categories that we…
In-Memory Compute and Data Grids serve as the fundamental components of an in-memory architecture. The objective of In-Memory Data Grids (IMDG) is to ensure exceptionally high data availability by storing it in memory in a highly distributed and parallelized manner. By loading terabytes of data into memory, IMDGs can effectively handle most of the requirements for processing Big Data today.…
Telecommunication companies can transform their operations into data-driven enterprises by utilizing the Digital Integration Hub Architecture, which is built on GridGain's in-memory computing platform. In this blog post, we will explore how the Digital Integration Hub architecture can assist telecommunication companies in enhancing customer insights, creating additional revenue streams, and…
MySQL is a popular open-source relational database management system (RDBMS) that has certain limitations when it comes to Big Data analytics. In this context, this post will delve into five key MySQL limitations that can be addressed by taking a modern approach to the data architecture.
1. Delivering Hot Data
In large Big Data analytics applications, the data cache stored in RAM can grow very…
Held on June 6, 2023, this year's Ignite Summit showcased the breadth and flexibility that make Apache Ignite a foundational technology for many demanding data use cases. Presenters represented a wide range of organizations, from financial services and telecommunications to programming language development.
Organized by GridGain and the Apache Ignite community, Ignite Summits attract engineers…
Why is it necessary to distribute data?
As systems that require data storage and processing evolve, they often reach a point where either the amount of data exceeds the storage capacity, or the workload surpasses the capabilities of a single server.
In such situations, there are two useful data distribution solutions: data sharding and migrating to a distributed database. Both solutions…
Apache Ignite has the ability to scale horizontally, allowing you to handle the data generated by your applications and services. When your Apache Ignite cluster is using excessive memory, you can utilize horizontal scaling, which is one of the fundamental architectural capabilities of Ignite. While the common advice is to "throw more resources into the cluster," it is often not practical or…