Amazon DynamoDB vs. Google Cloud Datastore vs. Redis Software

It’s great for server less and real-time applications. It would be great for gaming and mobile apps. However, if you need relational database and have fixed budget, do not use it. While budget can be managed, you need to be careful. Also this is not a tool for storing big data, there are other wide-column database types you could use for it ins the ad

I feel like Google Cloud Datastore is well suited when the data models are simple and the possible filtering and sorting operation are not complex. With more complex filtering, the amount of required indexes increase, and so does the costs.

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Redis has been a great investment for our organization as we needed a solution for high speed data caching. The ramp up and integration was quite easy. Redis handles automatic failover internally, so no crashes provides high availability. On the fly scaling scale to more/less cores and memory as and when needed.

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• To manage varying workloads, it enables users to increase capacity as necessary and decrease it as needed.
• Users can take advantage of its auto-scaling, in-memory caching, and backup without paying for the services of a database administrator.
• We can use it for low scale operations.

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• It is hosted in GCP, our primary cloud provider.
• It integrates with all the services we use in GCP.

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• Easy for developers to understand. Unlike Riak, which I've used in the past, it's fast without having to worry about eventual consistency.
• Reliable. With a proper multi-node configuration, it can handle failover instantly.
• Configurable. We primarily still use Memcache for caching but one of the teams uses Redis for both long-term storage and temporary expiry keys without taking on another external dependency.
• Fast. We process tens of thousands of RPS and it doesn't skip a beat.

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• Cost model may not be easy to control and may lead to higher costs if not carefully planned
• Indexing may be a cost culprit when not planned, because it's not included on the data costs
• The Query Language may not fulfill everybody's expectations, as it has less features than those of competitors.

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• Part of the Google Cloud Platform walled garden.
• Pay for read/writes to the datastore, in addition to storage.
• Not many libraries exist.

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• We had some difficulty scaling Redis without it becoming prohibitively expensive.
• Redis has very simple search capabilities, which means its not suitable for all use cases.
• Redis doesn't have good native support for storing data in object form and many libraries built over it return data as a string, meaning you need build your own serialization layer over it.

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It's core to our business, we couldn't survive without it. We use it to drive everything from FTP logins to processing stories and delivering them to clients. It's reliable and easy to query from all of our pipeline services. Integration with things like AWS Lambda makes it easy to trigger events and run code whenever something changes in the database.

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I give Google Cloud a full score because it satisfies our needs so well. We host most of our infrastructure on Google Cloud and using Google Cloud Datastore helps us to solve our NoSQL storage problem. and Google Cloud Datastore is so scalable and elastic. It saves us lots of time to maintain and saves us money.

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We will definitely continue using Redis because: 1. It is free and open source. 2. We already use it in so many applications, it will be hard for us to let go. 3. There isn't another competitive product that we know of that gives a better performance. 4. We never had any major issues with Redis, so no point turning our backs.

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Functionally, DynamoDB has the features needed to use it. The interface is not as easy to use, which impacts its usability. Being familiar with AWS in general is helpful in understanding the interface, however it would be better if the interface more closely aligned with traditional tools for managing datastores.

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The simple implementation, together with the Google Cloud dashboard that allows data inspection, editing and querying, makes Google Cloud Datastore a comprehensive choice when it comes to NoSQL databases.

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It is quite simple to set up for the purpose of managing user sessions in the backend. It can be easily integrated with other products or technologies, such as Spring in Java. If you need to actually display the data stored in Redis in your application this is a bit difficult to understand initially but is possible.

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It works very well across all the regions and response time is also very quick due to AWS's internal data transfer. Plus if your product requires HIPPA or some other regulations needs to be followed, you can easily replicate the DB into multiple regions and they manage all by it's own.

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I have not had to contact support for this service, however I have had to contact AWS for other services and their support has been good.

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The support team has always been excellent in handling our mostly questions, rarely problems. They are responsive, find the solution and get us moving forward again. I have never had to escalate a case with them. They have always solved our problems in a very timely manner. I highly commend the support team.

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Whitelisting of the AWS lambda functions.

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The only thing that can be compared to DynamoDB from the selected services can be Aurora. It is just that we use Aurora for High-Performance requirements as it can be 6 times faster than normal RDS DB. Both of them have served as well in the required scenario and we are very happy with most of the AWS services.

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If deploying an application in Google Cloud Platform, using Google Cloud Datastore is a no brainer because of the simplicity of setup. Other options would require more setup and configuration, and do not come with the same level of guaranteed uptime as Google Cloud Datastore. In our case, we've been using Google Cloud Datastore for 4 years without any issues and have never had to think about it.

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We are big users of MySQL and PostgreSQL. We were looking at replacing our aging web page caching technology and found that we could do it in SQL, but there was a NoSQL movement happening at the time. We dabbled a bit in the NoSQL scene just to get an idea of what it was about and whether it was for us. We tried a bunch, but I can only seem to remember Mongo and Couch. Mongo had big issues early on that drove us to Redis and we couldn't quite figure out how to deploy couch.

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I have taken one point away due to its size limits. In case the application requires queries, it becomes really complicated to read and write data. When it comes to extremely large data sets such as the case in my company, a third-party logistics company, where huge amount of data is generated on a daily basis, even though the scalability is good, it becomes difficult to manage all the data due to limits.

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• Some developers see DynamoDB and try to fit problems to it, instead of picking the best solution for a given problem. This is true of any newer tool that people are trying to adopt.
• It has allowed us to add more scalability to some of our systems.
• As with any new technology there was a ramp up/rework phase as we learned best practices.

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• It solves our NoSQL database problem.
• It it highly scalable and available to meets our needs.

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• Redis has helped us increase our throughput and server data to a growing amount of traffic while keeping our app fast. We couldn't have grown without the ability to easily cache data that Redis provides.
• Redis has helped us decrease the load on our database. By being able to scale up and cache important data, we reduce the load on our database reducing costs and infra issues.
• Running a Redis node on something like AWS can be costly, but it is often a requirement for scaling a company. If you need data quickly and your business is already a positive ROI, Redis is worth the investment.

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