Alternatives to Presto

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

As a frontend engineer on the Algorithms & Analytics team at Stitch Fix, I work with data scientists to develop applications and visualizations to help our internal business partners make data-driven decisions. I envisioned a platform that would assist data scientists in the data exploration process, allowing them to visually explore and rapidly iterate through their assumptions, then share their insights with others. This would align with our team's philosophy of having engineers "deploy platforms, services, abstractions, and frameworks that allow the data scientists to conceive of, develop, and deploy their ideas with autonomy", and solve the pain of data exploration.

The final product, code-named Dora, is built with React, Redux.js and Victory, backed by Elasticsearch to enable fast and iterative data exploration, and uses Apache Spark to move data from our Amazon S3 data warehouse into the Elasticsearch cluster.

I have been working on a Java application to demonstrate the latency for the select/insert/update operations on KUDU storage using Apache Kudu API - Java based client. I have a few queries about using Apache Kudu API

1. Do we have JDBC wrapper to use Apache Kudu API for getting connection to Kudu masters with connection pool mechanism and all DB operations?

2. Does Apache KuduAPI supports order by, group by, and aggregate functions? if yes, how to implement these functions using Kudu APIs.

3. How can we add kudu predicates to Kudu update operation? if yes, how?

4. Does Apache Kudu API supports batch insertion (execute the Kudu Insert for multiple rows at one go instead of row by row)? (like Kudusession.apply(List);)

5. Does Apache Kudu API support join on tables?

6. which tool is preferred over others (Apache Impala /Kudu API) for read and update/insert DB operations?

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!

My background is in Data analytics in the telecom domain. Have to build the database for analyzing large volumes of CDR data so far the data are maintained in a file server and the application queries data from the files. It's consuming a lot of resources queries are taking time so now I am asked to come up with the approach. I planned to rewrite the app, so which database needs to be used. I am confused between MongoDB and Druid.

So please do advise me on picking from these two and why?

My process is like this: I would get data once a month, either from Google BigQuery or as parquet files from Azure Blob Storage. I have a script that does some cleaning and then stores the result as partitioned parquet files because the following process cannot handle loading all data to memory.

The next process is making a heavy computation in a parallel fashion (per partition), and storing 3 intermediate versions as parquet files: two used for statistics, and the third will be filtered and create the final files.

I make a report based on the two files in Jupyter notebook and convert it to HTML.

• Everything is done with vanilla python and Pandas.
• sometimes I may get a different format of data
• cloud service is Microsoft Azure.

What I'm considering is the following:

Get the data with Kafka or with native python, do the first processing, and store data in Druid, the second processing will be done with Apache Spark getting data from apache druid.

the intermediate states can be stored in druid too. and visualization would be with apache superset.

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.

Our whole DevOps stack consists of the following tools:

• GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
• Respectively Git as revision control system
• SourceTree as Git GUI
• Visual Studio Code as IDE
• CircleCI for continuous integration (automatize development process)
• Prettier / TSLint / ESLint as code linter
• SonarQube as quality gate
• Docker as container management (incl. Docker Compose for multi-container application management)
• VirtualBox for operating system simulation tests
• Kubernetes as cluster management for docker containers
• Heroku for deploying in test environments
• nginx as web server (preferably used as facade server in production environment)
• SSLMate (using OpenSSL) for certificate management
• Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
• PostgreSQL as preferred database system
• Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

• Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
• Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
• Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
• Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
• Scalability: All-in-one framework for distributed systems.
• Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

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.