Alternatives to NuoDB

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.

1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

#InMemoryDatabases #DataStores #Databases

Trying to establish a data lake(or maybe puddle) for my org's Data Sharing project. The idea is that outside partners would send cuts of their PHI data, regardless of format/variables/systems, to our Data Team who would then harmonize the data, create data marts, and eventually use it for something. End-to-end, I'm envisioning:

1. Ingestion->Secure, role-based, self service portal for users to upload data (1a. bonus points if it can preform basic validations/masking)
2. Storage->Amazon S3 seems like the cheapest. We probably won't need very big, even at full capacity. Our current storage is a secure Box folder that has ~4GB with several batches of test data, code, presentations, and planning docs.
3. Data Catalog-> AWS Glue? Azure Data Factory? Snowplow? is the main difference basically based on the vendor? We also will have Data Dictionaries/Codebooks from submitters. Where would they fit in?
4. Partitions-> I've seen Cassandra and YARN mentioned, but have no experience with either
5. Processing-> We want to use SAS if at all possible. What will work with SAS code?
6. Pipeline/Automation->The check-in and verification processes that have been outlined are rather involved. Some sort of automated messaging or approval workflow would be nice
7. I have very little guidance on what a "Data Mart" should look like, so I'm going with the idea that it would be another "experimental" partition. Unless there's an actual mart-building paradigm I've missed?
8. An end user might use the catalog to pull certain de-identified data sets from the marts. Again, role-based access and self-service gui would be preferable. I'm the only full-time tech person on this project, but I'm mostly an OOP, HTML, JavaScript, and some SQL programmer. Most of this is out of my repertoire. I've done a lot of research, but I can't be an effective evangelist without hands-on experience. Since we're starting a new year of our grant, they've finally decided to let me try some stuff out. Any pointers would be appreciated!

Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com

We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.

We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...

ASP.NET / Node.js / Laravel. ......?

Please guide us

I recently started a new position as a data scientist at an E-commerce company. The company is founded about 4-5 years ago and is new to many data-related areas. Specifically, I'm their first data science employee. So I have to take care of both data analysis tasks as well as bringing new technologies to the company.

1. They have used Elasticsearch (and Kibana) to have reporting dashboards on their daily purchases and users interactions on their e-commerce website.

2. They also use the Oracle database system to keep records of their daily turnovers and lists of their current products, clients, and sellers lists.

3. They use Data-Warehouse with cockpit 10 for generating reports on different aspects of their business including number 2 in this list.

At the moment, I grab batches of data from their system to perform predictive analytics from data science perspectives. In some cases, I use a static form of data such as monthly turnover, client values, and high-demand products, and run my predictive analysis using Python (VS code). Also, I use Google Datastudio or Google Sheets to present my findings. In other cases, I try to do time-series analysis using offline batches of data extracted from Elastic Search to do user recommendations and user personalization.

I really want to use modern data science tools such as Apache Spark, Google BigQuery, AWS, Azure, or others where they really fit. I think these tools can improve my performance as a data scientist and can provide more continuous analytics of their business interactions. But honestly, I'm not sure where each tool is needed and what part of their system should be replaced by or combined with the current state of technology to improve productivity from the above perspectives.

When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

Hello, I am building a website for a school that's used by students to find Zoom meeting links, view their marks, and check course materials. It is also used by the teachers to put the meeting links, students' marks, and course materials.

I created a similar website using HTML, CSS, PHP, and MySQL. Now I want to implement this project using some frameworks: Next.js, ExpressJS and use PostgreSQL instead of MYSQL

I want to have some advice on whether these are enough to implement my project.

Hi everyone. I'm trying to create my personal syslog monitoring.

1. To get the logs, I have uncertainty to choose the way: 1.1 Use Logstash like a TCP server. 1.2 Implement a Go TCP server.

2. To store and plot data. 2.1 Use Elasticsearch tools. 2.2 Use InfluxDB and Grafana.

I would like to know... Which is a cheaper and scalable solution?

Or even if there is a better way to do it.