![]() ![]() You can run complex queries against loads of data (in terabytes or petabytes) of structured data using very sophisticated query optimization & massively parallel query execution.Redshift gives you fast querying capability over structured data using familiar SQL based clients & BI Tools.It has the following features that make it pretty useful compared to other Amazon Data Warehouses like Amazon S3, RDS, or Amazon DynamoDB. Table of ContentsĪmazon Redshift is a fast, fully managed cloud data warehouse that makes it simple & cost-effective to analyze all of the data using standard SQL and the existing Business Intelligence (BI) tools. In this article, you will learn about the importance of the Amazon Redshift Unload command along with the syntax and some examples. It can be used to analyze data in BI tools.Īmazon Redshift Unload saves the query result in Apache Parquet format that is 2x faster and consumes 6x less storage. Amazon Redshift Unload helps users to save the result of query data into Amazon S3. There are several instances where Data Scientists or Data Analysts need to analyze a smaller chunk of a large dataset but not small enough. Second Example:- Unload Table to Encrypted Files.First Example:- Unload Table to a CSV File.Standard Redshift Unload Command Parameters.Hevo, A Simpler Alternative to Integrate your Data for Analysis. ![]() If you’ like to learn more you can view the detailed documentation on the AWS doc site. ![]() There are a good number of advanced options for handing different delimiters, compressing your output files with GZIP and so on. UNLOAD ('select * from my_schema.my_table') Here’s what our UNLOAD statement looks in that case. If it’s larger, you’ll get one file for every 6.2 GB of your data when “parallel off” is set. If that’s not ideal for you, you can use the “parallel off” option and get a single file as long as the total file size is less than or equal to 6.2 GB. In our example you might end up with something like this. That’s because by default UNLOAD writes data out in parallel and creates multiple files. One thing that might jump out is our file name is “file_”. ![]() Just modify the SELECT statement on the first line. Of course you can unload only a subset of the data in the table. Here we go! UNLOAD ('select * from my_schema.my_table') Let’s take all of the data from my_table and put it into our S3 bucket. Now, let’s do the reverse of the COPY example above. There are instructions for getting it set up in that post. I suggest doing the same for the UNLOAD command we’re about to use. Iam_role 'arn:aws:iam::482569874589:role/RedshiftLoaderRole’ Īlso in that post, and in the example, I used an IAM role for authentication. In my post about the COPY command, I provided the following example of moving the contents into a csv file named “file.csv” and inserting it into a table called “my_table”. If you’ve used the COPY command, you’ll feel right at home with UNLOAD. Again, Redshift might have the data they want, but it’s probably not the right system for them to query directly. The UNLOAD command is quite efficient at getting data out of Redshift and dropping it into S3 so it can be loaded into your application database.Īnother common use case is pulling data out of Redshift that will be used by your data science team or in a machine learning model that’s in production. The good news is that Redshift is already doing the hard work of transforming the data you need for the dashboard, so all you need to do it move it from Redshift to the application database serving the API. The API is probably sitting on top of a database that’s better suited for such operations. While Redshift is great for large operations like crunching that data, concurrency isn’t its strong-suit. However, after the final step (Transform), you’re left with datasets that are not only valuable in your warehouse, but to other systems that you don’t want querying Redshift directly.įor example, you might have a REST API that serves pre-crunched data to your website for some kind of customer facing dashboard. In ELT, the data being loaded into your Redshift cluster is quite raw. It’s fairly obvious to most why you’d bring data from S3 into your Redshift cluster, but why do the reverse? In a modern data warehouse, you’re likely (hopefully!) taking an ELT rather than ETL approach in your processing. In this post, I’ll talk about the reverse – moving data from Redshift into S3 with the UNLOAD command. In a previous post, I wrote about using the COPY command to load data from an S3 bucket into a Redshift table. ![]()
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