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Data versioning and reproducible ML with DVC and MLflow
Categories: Data Science, DevOps & SRE, Events | Tags: Data Engineering, Git, Databricks, Delta Lake, Machine Learning, MLflow, Storage
Our talk on data versioning and reproducible Machine Learning proposed to the Data + AI Summit (formerly known as Spark+AI) is accepted. The summit will take place online the 17-19th November…
Sep 30, 2020
Version your datasets with Data Version Control (DVC) and Git
Categories: Data Science, DevOps & SRE | Tags: DevOps, Git, Infrastructure, Operation, SCM
Using a Version Control System such as Git for source code is a good practice and an industry standard. Considering that projects focus more and more on data, shouldn’t we have a similar approach such…
By Grégor JOUET
Sep 3, 2020
Machine Learning model deployment
Categories: Big Data, Data Engineering, Data Science, DevOps & SRE | Tags: DevOps, Operation, AI, Cloud, Machine Learning, MLOps, On-premises, Schema
“Enterprise Machine Learning requires looking at the big picture … from a data engineering and a data platform perspective,” lectured Justin Norman during the talk on the deployment of Machine…
Sep 30, 2019
Auto-scaling Druid with Kubernetes
Categories: Big Data, Business Intelligence, Containers Orchestration | Tags: EC2, Druid, CNCF, Container Orchestration, Data Analytics, Helm, Metrics, OLAP, Operation, Prometheus, Cloud, Kubernetes, Python
Apache Druid is an open-source analytics data store which could leverage the auto-scaling abilities of Kubernetes due to its distributed nature and its reliance on memory. I was inspired by the talk…
Jul 16, 2019
Clusters and workloads migration from Hadoop 2 to Hadoop 3
Categories: Big Data, Infrastructure | Tags: HDFS, Slider, Spark, YARN, Docker, Erasure Coding, Rolling Upgrade
Hadoop 2 to Hadoop 3 migration is a hot subject. How to upgrade your clusters, which features present in the new release may solve current problems and bring new opportunities, how are your current…
Jul 25, 2018
Data Lake ingestion best practices
Categories: Big Data, Data Engineering | Tags: Avro, Hive, NiFi, ORC, Spark, Data Governance, HDF, Operation, Protocol Buffers, Data Lake, File Format, Registry, Schema
Creating a Data Lake requires rigor and experience. Here are some good practices around data ingestion both for batch and stream architectures that we recommend and implement with our customers…
By David WORMS
Jun 18, 2018
Running Enterprise Workloads in the Cloud with Cloudbreak
Categories: Big Data, Cloud Computing, DataWorks Summit 2018 | Tags: Cloudbreak, HDP, Operation, Hadoop, AWS, GCP, Azure, OpenStack
This article is based on Peter Darvasi and Richard Doktorics’ talk Running Enterprise Workloads in the Cloud at the DataWorks Summit 2018 in Berlin. It presents Hortonworks’ automated deployment tool…
May 28, 2018
Ambari - How to blueprint
Categories: Big Data, DevOps & SRE | Tags: Ambari, Ranger, Automation, DevOps, Operation, REST
As infrastructure engineers at Adaltas, we deploy Hadoop clusters. A lot of them. Let’s see how to automate this process with REST requests. While really handy for deploying one or two clusters, the…
Jan 17, 2018
Advanced multi-tenant Hadoop and Zookeeper protection
Categories: Big Data, Infrastructure | Tags: Zookeeper, DoS, iptables, Operation, Scalability, Clustering, Consensus
Zookeeper is a critical component to Hadoop’s high availability operation. The latter protects itself by limiting the number of maximum connections (maxConns = 400). However Zookeeper does not protect…
Jul 5, 2017