GridGain In-Memory Computing Platform - Enterprise Edition
GridGain SystemsReviews from AWS customer
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Powerful In-Memory Computing Platform with Strong Performance
What do you like best about the product?
GridGain offers exceptional in-memory data grid capabilities that significantly improve application performance. The integration with existing Java code is smooth, and the support for distributed caching and real-time analytics is excellent. I also appreciate the robust scalability features and support for SQL queries.
What do you dislike about the product?
The learning curve is steep, especially for newcomers to distributed systems. The documentation, while extensive, could benefit from more hands-on examples and clearer navigation. Also, some configuration aspects feel overly complex and require deep technical knowledge.
What problems is the product solving and how is that benefiting you?
GridGain helps reduce latency for high-volume, data-intensive applications. It solves performance bottlenecks by enabling in-memory data access and processing, leading to faster analytics and real-time decision-making in critical systems.
Supercharged Data Access for Smarter, Faster Strategy
What do you like best about the product?
The speed. GridGain genuinely changes how fast you can access and act on data. For someone in a strategic role, that’s a game-changer. You can get real-time answers and model scenarios on the go.
What do you dislike about the product?
The learning curve was a bit steep at first. It’s not plug-and-play, we needed support to get everything integrated properly, if your backend is outdated, you might not unlock the full benefits immediately cause it does require a well structured architecture if you will.
What problems is the product solving and how is that benefiting you?
It's mainly helping me access data quickly so no more delays in forecasting or decision-making, plus it helped getting a unified view for different departments thus allowing us to pivot scenarios much quicker than before.
High-Performance In Memory Computing
What do you like best about the product?
its exceptional in-memory computing performance and real-time processing capabilities. It allows for lightning-fast data access and distributed computation, which significantly improves the performance of applications dealing with large-scale transactional and analytical workloads. The platform’s support for standard SQL and seamless integration with existing databases makes it highly practical for enterprise environments.
What do you dislike about the product?
it offers outstanding performance, it comes with a steep learning curve and can be complex to set up and configure. Initial deployment requires in-depth technical expertise, especially when tuning memory usage and configuring clusters for optimal performance.
What problems is the product solving and how is that benefiting you?
solves the problem of slow data access and processing in high-throughput environments by enabling in-memory data storage and distributed computing.
Powerful In-Memory Computing for High-Performance Apps
What do you like best about the product?
GridGain stands out for its speed, particularly for real-time, large-scale data processing. I have been impressed with its distributed in-memory data grid, which really speeds up any applications. The SQL support over the in-memory store has been particularly useful and the integration with the existing databases has been seamless.
What do you dislike about the product?
GridGain is a powerful tool but the learning curve is very steep since getting it set up in a more complex environment is difficult. The documentation can definitely be geared to new users better. It also takes quite a long time to configure and tune it initially, which may or may not be suited to teams with little background in distributed systems.
What problems is the product solving and how is that benefiting you?
GridGain provides me with the solution I am looking for to offload my processing of large amounts of data with low latency. It gives me an in-memory data layer, which speeds up application response times, and therefore user experience. Additionally, it decreases load on traditional databases.
GridGain Delivers Real-Time Power for Data-Driven Applications
What do you like best about the product?
What I like best about GridGain is its seamless combination of in-memory speed with distributed scalability and enterprise-grade features.
What do you dislike about the product?
What I dislike about GridGain—or where it can fall short—includes the following:
Steep Learning Curve:
The architecture is powerful but complex. New users often struggle with concepts like data affinity, collocated processing, and distributed joins.
Heavyweight Compared to Simple Caches:
For basic caching needs, GridGain (and Ignite) can feel like overkill compared to lightweight solutions like Redis or Memcached.
Advanced Features Behind Paywall:
Many enterprise features (security, monitoring, rolling upgrades) are only in the Enterprise or Ultimate editions, not in the Community Edition.
GridGain is powerful, but its complexity, configuration overhead, and limited free-tier features can be barriers to adoption.
Steep Learning Curve:
The architecture is powerful but complex. New users often struggle with concepts like data affinity, collocated processing, and distributed joins.
Heavyweight Compared to Simple Caches:
For basic caching needs, GridGain (and Ignite) can feel like overkill compared to lightweight solutions like Redis or Memcached.
Advanced Features Behind Paywall:
Many enterprise features (security, monitoring, rolling upgrades) are only in the Enterprise or Ultimate editions, not in the Community Edition.
GridGain is powerful, but its complexity, configuration overhead, and limited free-tier features can be barriers to adoption.
What problems is the product solving and how is that benefiting you?
GridGain solves several critical problems in high-performance data processing, especially in distributed, real-time environments. Here's a breakdown of the main problems it addresses and how they benefit you:
Problem: Slow Data Access from Disk-Based Databases
Traditional databases are too slow for real-time apps due to disk I/O latency.
GridGain Benefit:
Stores data in-memory across a cluster for microsecond response times, enabling real-time analytics, fraud detection, and responsive user experiences.
Problem: Scaling Bottlenecks in Centralized Systems
Single-node systems can't handle growing data and user load efficiently.
GridGain Benefit:
Its distributed architecture scales horizontally, letting you add nodes as demand grows, ensuring high availability and elasticity.
Problem: Separation of OLTP and OLAP Systems (HTAP Gap)
Using different systems for transactions and analytics creates latency, duplication, and complexity.
GridGain Benefit:
Supports Hybrid Transactional/Analytical Processing (HTAP), so you can query live transactional data in real time, reducing architecture complexity.
GridGain solves performance, scalability, and latency issues in data-intensive systems, enabling real-time processing and simplifying complex architectures.
Problem: Slow Data Access from Disk-Based Databases
Traditional databases are too slow for real-time apps due to disk I/O latency.
GridGain Benefit:
Stores data in-memory across a cluster for microsecond response times, enabling real-time analytics, fraud detection, and responsive user experiences.
Problem: Scaling Bottlenecks in Centralized Systems
Single-node systems can't handle growing data and user load efficiently.
GridGain Benefit:
Its distributed architecture scales horizontally, letting you add nodes as demand grows, ensuring high availability and elasticity.
Problem: Separation of OLTP and OLAP Systems (HTAP Gap)
Using different systems for transactions and analytics creates latency, duplication, and complexity.
GridGain Benefit:
Supports Hybrid Transactional/Analytical Processing (HTAP), so you can query live transactional data in real time, reducing architecture complexity.
GridGain solves performance, scalability, and latency issues in data-intensive systems, enabling real-time processing and simplifying complex architectures.
Revolutionizing Our Data Management with GridGain
What do you like best about the product?
GridGain’s powerful in-memory computing has drastically sped up our data processing. Its seamless integration with various data sources and excellent scalability ensure we get real-time insights quickly. The support team is responsive, and the comprehensive documentation makes implementation straightforward.
What do you dislike about the product?
The initial setup can be a bit complex, and the cost might be high for smaller businesses. However, once up and running, GridGain proves to be an invaluable tool.
What problems is the product solving and how is that benefiting you?
GridGain is solving our challenge of slow data processing times and inefficiencies in handling large-scale data. By accelerating data access and computation, it allows us to make faster, data-driven decisions, improving our operational efficiency and competitiveness.
High-speed data processing and analytics for our AI and ML models
What do you like best about the product?
We have been working with GridGain for about a year to help us continuously train our AI and ML models. Their Continuous Learning Framework supports machine learning and deep learning model training on operational data, with built-in machine learning libraries that facilitate training models using real-time data. GridGain also provides us with high-speed data processing and analytics, and near-linear scalability which have been key to helping us handle large datasets and perform the complex computations required in AI and ML training.
What do you dislike about the product?
We’ve experienced minor issues with some of our data integrations when it came to scaling our AI and ML models.
What problems is the product solving and how is that benefiting you?
GridGain helps us train and deploy our AI and ML models without costly infrastructure or complex configurations. The in-memory computing delivers high-speed processing at scale which has been key to our handling massive datasets and performing the complex computations we need in AI and ML training. Additionally, GridGain supports near real-time continuous learning against massive amounts of data, eliminating the need for data movement.
GridGain for modern event stream processing
What do you like best about the product?
- In-stream analystics and transaction processing all in-memory
- transaction persistence to disk
- in-memory first architecture
- high-performance at very low latency
- outstanding professional services and customer support
- new v8.9 integrations with Parquet files, Iceberg tables, and JSON documents
- transaction persistence to disk
- in-memory first architecture
- high-performance at very low latency
- outstanding professional services and customer support
- new v8.9 integrations with Parquet files, Iceberg tables, and JSON documents
What do you dislike about the product?
- some complexity...especially when implementing replication-based multi-cluster availability
- lots of features contribute to complexity
- more feature rich versus the competition = more expensive (but greater ROI)
- lots of features contribute to complexity
- more feature rich versus the competition = more expensive (but greater ROI)
What problems is the product solving and how is that benefiting you?
- rapid analysis of, and transactions on, increasing streaming data volumes web-based activitis and transaction
- accelerating decision making
- empowering decision support
- accelerating decision making
- empowering decision support
high performance, scalable
What do you like best about the product?
GridGain has enabled us to process and analyze large datasets much more rapidly than traditional disk-based systems. GridGain’s ability to scale horizontally allows us to handle increasing loads by simply adding more nodes to the cluster. By using GridGain as a caching layer, we've seen improvements in the performance of our customer-facing applications. Additionally, GridGain's support for SQL queries on cached data is awesome.
What do you dislike about the product?
You need to know what you are doing in order to setup Gridgain. Additionally, there is an inherent risk of data volatility in in-memory storage systems - do your homework and make sure you take into consideration backup and effective memory management practices like data persistence, cache eviction and expiry policies.
What problems is the product solving and how is that benefiting you?
GridGain has drastically improved our ability to process and analyze large volumes of data, leading to faster and more informed decision-making.
Huge performance increases for data processing and analytics
What do you like best about the product?
We used GridGain at my previous company to accelerate our risk and compliance software by speeding the underlying data caching, persistence, and compute layer. GridGain was able to reduce processing times for key calculations from minutes to milliseconds. We chose GridGain for its ANSI SQL support, ACID compliance, support for high volume transactions, and advanced persistence capabilities.
What do you dislike about the product?
GridGain is a powerful platform designed for complex data requirements. It has more features and is more advanced than competitive products. This, however, made it somewhat more difficult to configure leading to a longer-than-expected implementation. Once fully deployed, it proved to be a robust solution.
What problems is the product solving and how is that benefiting you?
GridGain takes care of our data caching, persistence, and the compute layer for custom analytics. GridGain has helped us simplify our data architecture in order to provide major performance improvements since we’re no longer moving data over the network.
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