InsightEdge Reviews

1. Low Latency

2. High throughput

3. Flexible options to implement

4. Fail-over and recovery is seamless.

5. Grid storage helps with data management

6. Built in pub sub helps with fast communication

7. Easy to scale ans load balance, if planned well.

1. Issues with notification delivery in the beginning of the project eventually resolved

2. Not out of the box for specific implementation, had to do sit through a bit of training to get full results.

3. Time zone difference does not help with support issues.

once stable, no issues

NA

In its core InsightEdge have the foundation that you need for an application - execution, messaging and storage. By design you also have partitioning/sharding of these three concepts. This together with its in-mem computing and fault tolerance gives us a platform with so much flexibility.

In-mem computing is complicated in terms of deployment since you have code and data in the same process. You need to have good tools to manage this in your environment.

We use InsightEdge as our main application platform so we are solving more or less all our business needs with InsightEdge. We get many benefits from InsightEdge but what really stands out with InsightEdge is performance and scalability.

As a long time user of Gigaspaces XAP (predecessor to InsightEdge) it seemed a natural step for Gigaspaces to step into analytics. InsightEdge has provided us with a single platform for both our transactional and analytic applications. We now have "realtime all the time" and the TCO is low. It's without any doubt one of, if not the, leading Enterprise platform that I would use for almost anything I do. As a professional service provider, we have tried many different solutions but the only one still making the cut is Gigaspaces InsightEdge and all the extra goodies that come with it. NO doubt "best of the best"

I dislike the fact that GIgaspaces is not a more official product. It is quite unknown which makes it harder to get new people on-board. I have also discovered that it's hard for some developers to grasp partitioning and routing

I would recommend you go all-in like we have thats when it shines the best

We build banking and insurance systems from scratch. We get linear scalability, incredible response times and built in hot-hot- failover to mention a few.

I like how it processes data fast with extremely low latency.It also has security & language interoperability with multi data storage & replication property.

It isnt marketed to the preferred audience so it is hard to locate.

It keeps businesses functional with consistent SLA with important apps. It also monitors and self heals apps. automatically & on demand.

Excellent architectural features that are worth calling out, such as:

Faster data access from partitioned memory grid

Hot & cold optimized caching

Ability to ingest real-time/historical data from diverse data sources such as Kafka, RDBMS systems & Object Stores with ease

Java/Spark-based data processing capabilities.

Event-based processing

Multi-model storage

Multiple language/framework support (Java/.Net/Rest/Spark/BI Tools/SQL) for data consumption

Scalability and Data replication options

Data Grid Fault Tolerance

Out of the Security and ability to extend the same

Multiple Dev-Ops Integration options

Support for Hybrid Cloud

The features mentioned above qualify it for a centralized Enterprise Data Integration Hub, especially for cases that require time/mission-critical real-time analytics.

In my humble opinion, multiple toned down versions with abiliity to switch off/on features can be built and marketed, for different audience personas, such as - Font End App Developers/Owners, Analytics Developes/Owners, Datawarehouse developers/Owners etc.

Great features. If you are looking for a unified and centralized Data Integration Hub as part of your data modernization efforts, have a look at Gigaspaces InsightEdge

It's a data-product in our portfolio on which we consult our customers across diverse domains.

It´s a platform out of the box that allows address end to end data use cases, including analytics and real-time data processing (both transactional and analytical)

GiGASPACES offers a large range of options using it and it´s could be not too clear how is the best way to combine it with already existing architectures.

Uses case where you need, in an agile and fast way, processing data in real-time including (both transactional and analytical). GIGASPACE helps to reduce the gap between the availability of fresh data and the needing for real-time decisions at the right moment.

The platform (XAP) is fast and stable and since its built in Java we can easilly integrate with the platform.

The platform has some core dependencies such as the underlying JDK, Spring and others, this blocks us from upgrade our services freely.

We use XAP as our primary data source, the platform enables us to build fast services providing real time data to our users.

We are using Gigaspaces XAP as our primary data storage solution for our entire online system. Since we adopted XAP in 2010 we have grown in customers, activity and system complexity many times over and our backend microservices powered by Gigaspaces has always been able to scale and handle every business demand we've thrown at it.

Gigasoaces XAP delivers very low latency and high scalability across our 1400+ microservices. It allows our developers to focus on functionality and business instead of code optimizations and complex design solutions.

Development of the core product seemed to slow down during a few years. This has changed dramatically during the last years, though!

It's our chosen solution for data storage and processing that powers all our 1400+ microservices — outstanding performance and scalability out of the box. It has easy-to-use programming interfaces that cater to everyday developer productivity.

Easy to start using the solution to prototype and test new ideas. Simplicity to add new features to the first solution delivered to answer new business needs.

Solving a capacity problem was the first motivation for implementation. Still, the choice of InsightEdge aimed to go much further and initiate a data-centric type transformation of several existing systems, leading to simplification, cost-cutting, and a high level of performance.

Even if single request processing time was not the challenge, we get perfect response time and tried to stress the system up to more than 2000 requests per second without noticing an impact on performance.

Knowing we can do a lot in many ways due to all InsightEdge capabilities (connectors, storage styles, ...), it can be tricky to find the best implementation for a given problem. Many will work, but if you need to have real-time top performance, you need to work on it and be ready to understand how the solution is managing memory.

Depending on your requirements and the volume of data stored, you may need a lot of memory in your virtual machines.

We solved a technical capacity problem first by implementing a cache where standard cache solutions were inefficient because too much variability in the requests was leading to very low cache hit performance. Feeding a cache would have required years of calculation and will be terabytes big, which was not realistic.

InsightEdge performance and flexibility permit us to process requests on the fly and to be able to try to correlate them with already calculated ones providing the same results, reducing drastically the need to call the target system (up to 90% in only a week, and progressively increasing towards 100%).

Once the cache was full of data, it is a high-performance data source used for marketing or technical very complex analysis.

The benefits were:

- solving in only 3 months the capacity problem

- being able to suppress code in other systems, using the cache as the reference source of information

- replace empirical semi-manual analysis with exhaustive and automated ones, replacing repetitive and time-consuming tasks for business experts

- transform the information system towards a more agile and efficient data-centric one.

An impressive tool to work with huge volume of data in memory, excellent performance but also a quick win in terms of deployment.

-Quick implementation.

-Excellent support

-Performance

-Resilence of the solution

Increasing their presence in the European market

Real time batch, problems with large volumes of data. discharge workload from mainframe systems