September 2021 Summaries

Census has integrated with Airtable, enabling business teams to build powerful workflows and automation using data directly from the data warehouse. This integration allows business teams to interact with data in their warehouse without needing SQL knowledge, while ensuring that every team is working off a single source of truth. Census helps automate processes, such as managing new orders and prioritizing leads, by syncing data to various destinations like Airtable, Active Campaign, and Salesforce. The integration also enables self-serve data access for business teams, reducing manual hours of work required to keep data up-to-date and accurate. By using Census and Airtable together, companies can streamline their operational workflows and make data-driven decisions more easily.

You can export a CSV file from BigQuery using three methods: the BigQuery console export, the bq command-line tool, or the client library in various programming languages such as Node.js, Java, and Python. The first method is straightforward, while the second option requires more effort but provides flexibility through optional flags. The third method takes the most effort to set up but allows for integration in automated workflows. Before exporting, be aware of limitations, including a 1 GB data limit per file and the inability to export nested and repeated data directly to CSV. Additionally, using tools like Census can simplify the process by providing reverse ETL capabilities that make it easy to send data from BigQuery to various destinations.

"`` As a non-technical data owner, it's essential to ask yourself what you want from your data discovery process, ensuring that the analysis outcome aligns with your business goals. You should consider questions like "What does a successful analysis outcome look like?" and "Is my data reporting the right things?" to identify potential biases and areas for improvement. Additionally, think about whether there are obvious sources of bias in your data and how you can enrich the discovery process with outside sources. By asking yourself these questions, you'll be able to provide valuable framing for your data scientists' discovery process, leading to more accurate insights and better business decisions.

You can export a CSV file from Redshift using four different methods: the UNLOAD command, the AWS SDK, the AWS command-line interface (CLI), and a SQL client. The UNLOAD command allows you to quickly export data with simple SQL queries, while the AWS SDK provides more flexibility and customization options. The AWS CLI offers another way to execute SQL queries and download data, and a SQL client can be used to connect to Redshift and export data directly from your local machine. These methods offer varying degrees of complexity and customization, allowing you to choose the best approach for your specific use case and needs.

This article discusses five essential date functions in SQL: DATE_TRUNC(), DATE_DIFF(), DATE_ADD()/DATE_SUB(), EXTRACT(), and CURRENT_DATE(). These functions enable data manipulation, comparison, and analysis of dates, which is crucial for businesses to make informed decisions. The examples provided by the fictional company Sandy Shores illustrate how these functions can be used to achieve specific goals, such as aggregating sales by month, calculating shipping times, and identifying active customers. By mastering these date functions, developers can write more efficient and effective SQL queries, ultimately improving their overall data analysis skills.