[Links of the Day] 16/06/2020 : Time Series Forecasting best practices, AWS account controller, Causality Arxiv papers

• Time Series Forecasting : Microsoft Time Series Forecasting Best Practices & Examples [github]
• Aws Account Controller : AWS Self-service creation and deletion of sandbox-style accounts. [github]
• Arxausality : a collection of every causality + machine learning paper submitted to arXiv in the recent past.

[Links of the Day] 11/06/2020 : Metric Time-Series Database, Machine Learning for metrics, Causal Time series Analysis

• Victoria Metrics : fast, cost-effective and scalable time-series database, if you need a backend for Prometheus, by example, this is the DB for you.
• Sieve : a platform to derive actionable insights from monitored metrics in distributed systems. the platform is composed of two separate systems. One geared toward trace reduction and selection with intelligent sampling using a form of zero-positive learning. And a second system that extracts correlations between the services generating the traces.
• Tigramite : causal time series analysis python package. It allows to efficiently reconstruct causal graphs from high-dimensional time-series datasets and model the obtained causal dependencies for causal mediation and prediction analyses [github]

[Links of the Day] 30/04/2020 : Time Series on MxNet, Intuitive Linear Algebra, NLP attacks

• Gluon Times series : Probabilistic time series modelling in Python using AWS MxNet.
• Intuitive Guide to linear algebra : Nice and simple introduction to linear algebra.
• TexFooler :A Model for Natural Language Attack on Text Classification and Inference. Repo contains example of BERT and ESIM attack. 

[Links of the Day] 28/04/2020 : Distributed Time Series Database, Data lakes, Translate data between format

• Modern data lakes : if you think you need a data lake, you probably don' need one and are better off using S3/athena or GCP/bigquery . If you know you want a data lake you might be mature enough to need one and should read this article.
• M3DB : Distributed Time Series database from Uber, it tries to address horizontal scaling of storage and queries or long term storage limitation of existing solutions.
• ConfBase : a practical tool for inferring and instantiating schemas and translate between data formats. The tools support JSON, GraphQL, YAML, TOML, and XML. [github]

[Links of the Day] 14/04/2020 : Time series dynamical attractors Autoencoder , Binarized Neural Network framework, Machine learning and Databases

• Deep learning of dynamical attractors from time series measurements : the authors propose a general embedding technique for time series, consisting of an autoencoder trained with a novel latent-space loss function. Worth giving it a look if you deal with time series.
• larq : open-source Python library for training neural networks with extremely low-precision weights and activations, such as Binarized Neural Networks. Basically, this framework is aiming at embedded / FPGA / ASIC machine learning models deployment. A fantastic resource and great model zoo on top of that.
• Cloudy with a chance of DBMS : Databases are going to embedded more and more machine learning solution. Big query from Google already does that. But it's just a question of time for most mainstream DB to offer ML service.

[Links of the Day] 19/11/2019 : Stateful Functions, Timeseries in JS, EU SMEs and Cloud

• Stateful functions : this is a really cool concept that mimics the lambda model but injects statefulness in the function by leveraging Apache Flink. Ok, you need the whole Kafka/Flink stack but the benefit from this is quite staggering.
• uplot : fast tiny time series and line chart. Enough said :) 
• Study on the economic detriment from unfair and unbalanced cloud computing contracts :  European study on the unfair balance of power held by cloud providers and its impacts on EU SMEs. Sadly the report seems to be heavily biased against the cloud providers. It seems to boil down that a lot of problem stems from a lack of education of the customer and the lack of understanding of the problems and costs associated with cloud-based services.

[Links of the Day] 12/10/2017 : bitcoin resource list, time series DB seminar, Microservices debugger

• Bitcoin resource list : extensive list of bitcoin resource ranging from basic introduction, history, tutorial, to in-depth tech materials
• Time Series Database Lecture : 2017 Carnegie Mellon university lectures. This is quite good as it not this series of lecture not only offer high-quality theoretical knowledge in the field but also invited talk from key commercial and opensource player in this field ( influxdb, timescale, etc..) 
• Squash : microservices debugger, because now you can't rely on your monolith debugging skill and tool set anymore ( ^_^).

[Links of the Day] 03/04/2017 : Conway's Game of life Clock, Human-Bot social interaction, SQL time series DB

• Digital clock in Conway's Game of Life : I can't even start to comprehend how you can design this. But this is beyond cool.
• Online Human-Bot Interactions: Detection, Estimation, and Characterization : An analysis of bots on socials network (twitter). I think we need a reverse Turing test. When a robot can detect when they talk to a human.... Reverse captcha to weed out that pesky meat-bag from meddling from our robotic overlord affairs.
• Timescale : SQL compatible time series database. Another competitor for Influxdb. Let's just say that the clustering feature will make or break it as Influxdb has some serious issue there [github]

[Links of the day] 14/11/2016 : Time series features extraction, GCHQ data analysis platform and Alibaba's Kafka contender : RocketMQ

TsFresh : Automatic extraction of relevant features from time series. This is really usefull for anybody dealing with metrics. It allow in one sweep data cleaning and feature extraction.
Stroom : GCHQ data processing storage and analysis platform.
RocketMQ : Alibaba's MQ proposed as an Apache project. It tries to solve some of the limitation of Kafka while providing better performance.

 

[Links of the day] 26/02/2016 : Usenix Fast 16 , FPGA liberouter and Event delivery at spotify

• Fast 2016: all of Usenix Fast 2016 goodness available in one place.Interesting to see that we start to see the emergence of storage systems optimized for time series [BTrDB] . Also note the always interesting report on failure rate (this time for flash)
• Liberouter : really cool project using FPGA to deliver hardware acceleration of network security and monitoring tools. 
• Event Delivery at spotify : part 1 of a series of blog post on event monitoring and management system used at spotify.

Links of the day 28/10/2015: K/V DB for side data, RDMA + HTM , metrics 2.0

• PalDB : embeddable write-once key-value store written in Java by LinkedIn crowd. This solution kind of sit between json/file and level/rocksdb [github]
• RDMA + HTM : very nice concept, would love to see the extension of HTM to NVM tech in order to close the loop. T His would deliver tremendous speed up capability. But also another type of concurrency model to understand. 
• Metrics 2.0 : emerging set of conventions, standards and concepts around time series metrics metadata

Links of the day 29/09/2015 : Time series in SQL, Chaos engineering, Distributed Systems Architecture

• Storing Time series in SQL : how to efficiently store time series data in postgres SQL or other SQL db
• Principles of Chaos Engineering : discipline of experimenting on a distributed system in order to build confidence in the system’s capability to withstand turbulent conditions in production.
• Architectural patterns of resilient distributed systems : while this is a good talk about resiliency I have a slight issue with the term itself. I have a preference for ductile rather than resilient. As often system that are designed to be resilient are extremely brittle when the reaching the breaking point [video] [slide-deck]

Links of the day 09 - 06 - 2015

Today's links 09/06/2015 : Local algorithm for highly scalable architecture, Artic time series Database on top of #MongoDB, KVM Forum

• Local algorithms : Very good survey of algorithms with the very interesting property that they runs in constant time, independently of the size of the network. Which makes then extremely attractive for large scale distributed systems.
• Artic : a high performance datastore for numeric data on top of mongoDB. It supports Pandas, numpy arrays and pickled objects out-of-the-box, with plug-gable support for other data types and optional versioning. They have wild claim in regard performance and scalability but until we get a proper time series benchmark out there it still hard to compare time series solutions.
• KVM forum 2015 :  Schedule of this year KVM forum. Interesting Bits: Real time stuff , some live migration with DPDK and running 1k guests per host.

Links of the day 20 - 05 - 2015

Today's links 20/05/2015: #BigData anomaly detection, Machine learning, Secure #Containers

Generic and Scalable Framework for Automated Time-series Anomaly Detection : Yahoo time series anaomaly detection framework.
A Few Useful Things to Know about Machine Learning : very good overview of machine learning, it summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.
Clear Containers : Intel effort to improve security of containers by using the VT-x technology.[LWN]

 

Links of the day 13 - 03 - 2015

Today's links 13/03/2015: machine learning , Time-Series and Geospatial database, Write acceleration for virtual environment

• Clustered Fault Tolerant Write Acceleration in a Virtualized Environment : PernixData present a fault tolerant host-side flash write acceleration layer that seamlessly integrates with the virtualized environment while preserving dynamic resource management and high availability
• Machine Learning for Programming : Peter Norvig keynotes on using machine learning techniques to solve more general software problems, helping both the advanced programmer and the novice one.
• Time-Series Database Requirements : the author present its own ideas about what constitutes a good time-series database
• Why Are Geospatial Databases So Hard To Build? : the root of the problem is that the internals of a database engine designed for traditional “text and numbers” data models, which is virtually all of them, has very little in common with a database engine designed for real-time spatial data models.

Links of the day 06 - 02 - 2015

Today's links 06/02/2015: Hashing, Distributed time series / monitoring , MTU, Spray list

• Perfect hashing : when you like your hash without collision 
• Prometheus : Some interesting concept such as data is sharded and local only for reliability and scalability, time series oriented storage and query system and expose time series via key value pair. 
• Path MTU discovery : when you want to know what is the biggest frame you can send when you do not own 100% of the network path ( or if you just forgot)
• Spray list : nice data structure delivering scalable relaxed priority queue[github implementation]