In this talk, we will cover Dask as the basis for a scalable Python ecosystem and then explore the SQL query engine dask-sql and how it might help to combine the best of both the SQL and Python world. After that, a demo session will demonstrate how you can set up dask-sql on your own cluster and quickly analyze your (big) data.
Accessing and working with data is increasingly important for the success of a project or a company. Traditionally, relational (SQL) databases have been used for this and many developers and non-developers working with data are very familiar with the SQL query language. However, the requirements of today's landscape are much more complicated: machine learning, complicated distributed clusters of machines, large amounts of data - all of this does not play well with the world of SQL databases.
The Dask ecosystem and the dask-sql library try to bridge this gap. Dask enables its users to easily scale data-intensive Python applications and comes with a large number of extensions, e.g. for machine learning. dask-sql adds a SQL query layer on top of the Dask ecosystem, which allows to query big and small data from SQL and scale the computations via a Dask cluster - without interacting with the complexities of a distributed system.
In this talk, we will cover Dask as the basis for a scalable Python ecosystem and then explore the SQL query engine dask-sql and how it might help to combine the best of both the SQL and Python world. After that, a demo session will demonstrate how you can set up dask-sql on your own cluster and quickly analyze your (big) data.
SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
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