What is Amazon Redshift?

Amazon Redshift is a petabyte-scale data warehouse hosted by Amazon Web Services (AWS). Unlike on-premise data warehouses, Amazon Redshift is a fully-managed service, which means users are relieved of the architectural and operational challenges that come with setting up and scaling a data warehouse.

Users scale their usage of Amazon Redshift in terms of clusters. Each cluster is a set of nodes: one leader node and one or more compute nodes, the exact number of which will depend on the size of data at hand, the number of queries that will be performed, and the desired level of query performance.

Amazon Redshift vs S3

Amazon Redshift and Amazon Simple Storage Service (Amazon S3) are often brought up in the same conversation, even confused for one another. But there’s a distinct difference between the two—Amazon Redshift is a data warehouse; Amazon S3 is object storage. Amazon S3 vs Redshift isn’t an either/or debate. In fact, many organizations will have both.

Amazon S3 vs Redshift can be summed up by allowing for unstructured vs structured data. As a data warehouse, the data that is ingested into Amazon Redshift must be structured. It’s an environment built for business intelligence tools and familiar SQL-based clients using standard ODBC and JDBC connections. Amazon S3, on the other hand, can receive any size or structure of data without requiring that the purpose of that data be defined up front. It provides a space for initial data exploration and discovery, which leads to increased analytic opportunities.

Publishing data from Amazon S3 to Redshift

Moving data from Amazon S3 to Redshift involves transforming raw data into its desired structure for use in AWS Redshift. There are three primary ways that organizations can do this:

  • Building a Redshift ETL Pipeline
  • Using Amazon’s managed ETL service, Glue
  • Using a data preparation platform

Due to their complexity, ETL tools and processes are most commonly managed by IT. They required extensive coding knowledge in order to move large batches of data from one place to another. Increasingly, however, organizations are relying less heavily on ETL because of the barrier they present.

ETL pipelines are expected to serve a huge variety of users across an organization, each of which require different data that has been cleansed and transformed differently. It’s unfair to burden a small IT team with the task of doing the data dirty work for an entire organization; instead, organizations are now involving their business users with aspects of traditional ETL vis-à-vis modern data preparation platforms like Alteryx Designer Cloud.

Designer Cloud Data Preparation Platform + AWS Redshift

With Designer Cloud’s intuitive, no-code interface, moving data from S3 into Redshift for data warehousing or preparing S3 data for machine learning and reporting can easily and quickly be accomplished by data professionals without reliance on IT departments.

Faster Than Hand Coding 

Designer Cloud’s automatically profiles your S3 data and highlights common discrepancies and anomalies, reducing the time to resolve these issues to seconds.

Automatic data and schema inference helps structure S3 data, and Designer Cloud’s proprietary machine learning technology detects potential join keys across multiple tables as well as missing values, mismatched values and outliers.

Automate Workflows 

Data can be refreshed on a recurring basis using Designer Cloud’s automation capabilities. Dynamic inputs allow you to define rules on which files to pick up every run and which ones to skip.

Webhook notifications allow for powerful interoperability with APIs inside and outside of AWS, and email notifications keep you up to date on what’s going on with the migration of your data.

Suggested Transformations 

Designer Cloud automatically interprets your data transformation intent when you select data using a proprietary machine learning algorithm that takes into consideration the most common activities our users have performed using the tool.

Simplifying Your AWS Redshift Data Pipeline

To accelerate and simplify the process of moving data from Amazon S3 into Amazon Redshift, organizations are leaving bulky ETL tools behind. Learn how swiping and clicking have become as powerful as writing code with Designer Cloud.

For more information on the Designer Cloud data preparation platform, discover your options here.

Performance and Scalability Matter

Delivering fast data pipeline speeds makes Designer Cloud the top choice for practicing on-demand applications such as Amazon S3 vs Redshift. Rather than spending company funding on the next round of costly investments, use Designer Cloud’s S3 to Redshift pipeline automation to instantly shift capacity up or down as needed with one simple settings change.

Simplifying Your Data Pipeline

To simplify your data pipeline you need speed and autonomy all in one. Learn how to start automating your data pipeline with the industry leading Designer Cloud, #1 in cloud data preparation. For more tools on optimizing your data centralization pipeline today, discover your options here.

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