Alternatives to Cayley

We have an in-house build experiment management system. We produce samples as input to the next step, which then could produce 1 sample(1-1) and many samples (1 - many). There are many steps like this. So far, we are tracking genealogy (limited tracking) in the MySQL database, which is becoming hard to trace back to the original material or sample(I can give more details if required). So, we are considering a Graph database. I am requesting advice from the experts.

1. Is a graph database the right choice, or can we manage with RDBMS?
2. If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
3. If Graph database(Neo4j, OrientDB, Azure Cosmos DB, Amazon Neptune, ArangoDB), which one is good, and what are the best practices?

I am sorry that this might be a loaded question.

I'm evaluating the use of RedisGraph vs Microsoft SQL Server 2019 graph features to build a social graph. One of the key criteria is high availability and cross data center replication of data. While Neo4j is a much-matured solution in general, I'm not accounting for it due to the cost & introduction of a new stack in the ecosystem. Also, due to the nature of data & org policies, using a cloud-based solution won't be a viable choice.

We currently use Redis as a cache & SQL server 2019 as RDBMS.

I'm inclining towards SQL server 2019 graph as we already use SQL server extensively as relational database & have all the HA and cross data center replication setup readily available. I still need to evaluate if it fulfills our need as a graph DB though, I also learned that SQL server 2019 is still a new player in the market and attempts to fit a graph-like query on top of a relational model (with node and edge tables). RedisGraph seems very promising. However, I'm not totally sure about HA, Graph data backup, cross-data center support.

We have an in-house build experiment management system. We produce samples as input to the next step, which then could produce 1 sample(1-1) and many samples (1 - many). There are many steps like this. So far, we are tracking genealogy (limited tracking) in the MySQL database, which is becoming hard to trace back to the original material or sample(I can give more details if required). So, we are considering a Graph database. I am requesting advice from the experts.

1. Is a graph database the right choice, or can we manage with RDBMS?
2. If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
3. If Graph database(Neo4j, OrientDB, Azure Cosmos DB, Amazon Neptune, ArangoDB), which one is good, and what are the best practices?

I am sorry that this might be a loaded question.

Hello All, I'm building an app that will enable users to create documents using ckeditor or TinyMCE editor. The data is then stored in a database and retrieved to display to the user, these docs can contain image data also. The number of pages generated for a single document can go up to 1000. Therefore by design, each page is stored in a separate JSON. I'm wondering which database is the right one to choose between ArangoDB and PostgreSQL. Your thoughts, advice please. Thanks, Kashyap

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

We have an in-house build experiment management system. We produce samples as input to the next step, which then could produce 1 sample(1-1) and many samples (1 - many). There are many steps like this. So far, we are tracking genealogy (limited tracking) in the MySQL database, which is becoming hard to trace back to the original material or sample(I can give more details if required). So, we are considering a Graph database. I am requesting advice from the experts.

1. Is a graph database the right choice, or can we manage with RDBMS?
2. If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
3. If Graph database(Neo4j, OrientDB, Azure Cosmos DB, Amazon Neptune, ArangoDB), which one is good, and what are the best practices?

I am sorry that this might be a loaded question.

I'm evaluating the use of RedisGraph vs Microsoft SQL Server 2019 graph features to build a social graph. One of the key criteria is high availability and cross data center replication of data. While Neo4j is a much-matured solution in general, I'm not accounting for it due to the cost & introduction of a new stack in the ecosystem. Also, due to the nature of data & org policies, using a cloud-based solution won't be a viable choice.

We currently use Redis as a cache & SQL server 2019 as RDBMS.

I'm inclining towards SQL server 2019 graph as we already use SQL server extensively as relational database & have all the HA and cross data center replication setup readily available. I still need to evaluate if it fulfills our need as a graph DB though, I also learned that SQL server 2019 is still a new player in the market and attempts to fit a graph-like query on top of a relational model (with node and edge tables). RedisGraph seems very promising. However, I'm not totally sure about HA, Graph data backup, cross-data center support.