![]() Second, DuckDB is highly optimized for analytical query workloads (OLAP). If you need to run a DB for local data analysis, it’s the way to go! Performances There is no client/server, and it is straightforward to install. On the other hand, DuckDB is embedded in the host application process and manages a database stored in a single file. In addition, the data transfer to/from the client is slower than it should be, especially for local installations. What does that mean? Unlike Postgres, there is no client/server to set up or external dependencies to install. Here are a few benefits of using DuckDB Localįirst, DuckDB is an in-process single-file database with no external dependencies. If it were easy, you would have everything to gain by using an RDBMS.ĭuckDB is the easiest and fastest way to analyze data with a DB. The question is, why? Setting up a database and loading data in it can be a painful, slow, and frustrating experience. But, instead of using a relational database management system (RDBMS), you use Pandas and Numpy. Interestingly, if you look at your operations, you usually perform database operations such as joins, aggregates, filters, etc. To achieve those things, you pull up a Jupyter notebook, import Numpy and Pandas, and then execute your queries. Why DuckDB? As a data analyst or a data scientist, a typical workflow is to load data from a CSV file or an S3 bucket, perform preprocessing steps, and run your analysis. How to retrieve data from a DuckDB query. ![]() How to query data with DuckDB and Python.
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