One of the first when working with ClickHouse is “How do I set up my own ClickHouse cluster™?” This guide is the answer.
Data backups are an inglorious but vital part of IT operations. They are most challenging in “big data” deployments, such as analytics databases. This article will explore the plumbing involved in backing up ClickHouse and introduce the clickhouse-backup tool for automating the process.
Readers of the Altinity blog know we love ClickHouse materialized views. Materialized views can compute aggregates, read data from Kafka, implement last point queries, and reorganize table primary indexes and sort order. Beyond these functional capabilities, materialized views scale well across large numbers of nodes and work on large datasets. They are one of the distinguishing features of ClickHouse.
Python is a force in the world of analytics due to powerful libraries like numpy along with a host of machine learning frameworks. ClickHouse is an increasingly popular store of data. As a Python data scientist you may wonder how to connect them. This post contains a review of the clickhouse-driver client. It’s a solidly engineered module that is easy to use and integrates easily with standard tools like Jupyter Notebooks and Anaconda. Clickhouse-driver is a great way to jump into ClickHouse Python connectivity.