Alternatives to Azure Databricks

From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.

I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.

Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

• Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
• Our blog: https://multithreaded.stitchfix.com/blog/
• Careers: https://multithreaded.stitchfix.com/careers/

#DataScience #DataStack #Data

I'm wondering if any Cloud Firestore users might be open to sharing some input and challenges encountered when trying to create a low-cost, low-latency data pipeline to their Analytics warehouse (e.g. Google BigQuery, Snowflake, etc...)

I'm working with a platform by the name of Estuary.dev, an ETL/ELT and we are conducting some research on the pain points here to see if there are drawbacks of the Firestore->BQ extension and/or if users are seeking easy ways for getting nosql->fine-grained tabular data

Please feel free to drop some knowledge/wish list stuff on me for a better pipeline here!

I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.

What's nice too is that it has SQL-based ML tools, and it has great GIS support!

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!

We are a young start-up with 2 developers and a team in India looking to choose our next ETL tool. We have a few processes in Azure Data Factory but are looking to switch to a better platform. We were debating Trifacta and Airflow. Or even staying with Azure Data Factory. The use case will be to feed data to front-end APIs.

CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

REST API for SaaS application

I'm currently developing an Azure Functions REST API with TypeScript, tsoa, Mongoose, and Typegoose that contains simple CRUD activities. It does the job and has type-safety as well as the ability to generate OpenAPI specs for me.

However, as the app scales up, there are more duplicated codes (for similar operations - like CRUD in each different model). It's also becoming more complex because I need to implement a multi-tenancy SaaS for both the API and the database.

So I chose to implement a repository pattern, and I have a "feeling" that .NET and C# will make development easier because, unlike TypeScript, it includes native support for Dependency Injection and great things like LINQ.

It wouldn't take much effort to migrate because I can easily translate interfaces and basic CRUD operations to C#. So, I'm looking for advice on whether it's worth converting from TypeScript to.NET.

Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark