The GridGain Systems In-Memory Computing Blog
In this article, we will focus on how Apache Ignite handles failover and recovery during transaction execution.
In the previous article in this series, we looked at concurrency modes and isolation levels. Here are topics we will cover in
the rest of this series:
Failover and recovery [this article]
Transaction handling at the level of Ignite persistence (WAL, checkpointing, and more)…
Today, we are thrilled to announce the release of GridGain Platform v8.9, adding new and enhanced integrations with popular data formats – including Apache Parquet, Apache Iceberg, CSV, and JSON – in order to enable more complete real-time analysis of your increasingly complex enterprise data. These enhancements make large volumes of enterprise data in data lakes and semi-structured document…
This article on pessimistic and optimistic concurrency is the second in the Apache Ignite Transactions Architecture series.
In the previous article, we looked at the two-phase commit protocol and how it worked with various types of cluster nodes in Apache Ignite. Here are topics we will cover in the rest of this series:
Pessimistic and optimistic concurrency [this article]…
This article on Apache Ignite and the two-phase commit protocol is the first in a series of five posts regarding the Apache Ignite transactions architecture.
Apache Ignite supports a range of different Application Programming Interfaces (APIs). In this multi-part article series, we will take a more detailed look at how Apache Ignite manages transactions in its key-value API and some of the…
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…
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…
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 /…
To improve application and query performance, developers frequently use summary tables. The summary table pattern is where we feed data into an Apache Ignite or GridGain cluster into two tables: the original data; and a summary, or rollup, of that data.
With GridGain’s SQL engine, it’s not always necessary to have a summary table! For many, perhaps most, use cases, the fact that the data is…
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…