Data. Big data is everywhere in your business and odds are good that you have petabytes of it. From your customer's purchasing information to financing data, you need to make sure that you are properly managing your data. This means working on recording, organizing, and analyzing it. As the old expression goes, "Junk in, junk out." If you don't properly manage your data, you'll have nothing but junk. This means that you have to store your data and datasets.

Amazon Redshift is one of the leading big data management services that any business can use to extract, transform and load data for various business uses. Amazon’s AWS platform is designed to help with that by providing access to Amazon Redshift with scalable AWS services. Redshift is complex, which gives you a lot of customization options but can also be harder to optimize without help. Here are 10 Amazon Redshift performance tips to maximize your Amazon Redshift experience.

Amazon Redshift is a fully managed data warehouse solution that allows you to efficiently analyze all your data using your existing business intelligence tools. While Amazon Redshift is one of the industry's top data storage solutions, many considerations need to be made before using AWS Redshift. One of the primary elements of any cloud-based storage solution is knowing how to transfer and secure data properly. Here, we will break down how to properly move data to and from the AWS Redshift platform.

Data marts are miniature, specialized data warehouses.

In a digitally powered economy, having access to data can help companies navigate market changes, perform customer analytics and adjust their strategy to meet demand. Unfortunately, most of the data company’s generate are unstructured and siloed across various departments in the organization. According to Forbes, 95 percent of businesses cite the need to manage unstructured data as a problem for their business.

There has never been a better time to start a startup, thanks to all of the advancements in communications and data management technology. Data engineering and utilization are at the core of every new startup that plans on disrupting and dominating its markets. Fortunately, Amazon Redshift can make the data management aspects of running a business much easier. Here are 11 Amazon Redshift tips for startups.

ETL is an acronym for Extract, Transform and Load. It refers to a process of extracting data from one system and transforming it so that it can be loaded into another system. It is the process that lets businesses amass large amounts of data in data warehouses that they can use for business operations. Reverse ETL is a term used when the data is pushed from the warehouse to the CRM. This process seems simple, but there are many pitfalls that can come up during this phase.

Azure Synapse Analytics, still commonly known as Azure Data Warehouse, is Microsoft's cloud data warehouse that processes relational and non-relational data for analytics. As one of the most popular data warehousing tools, Azure lets you generate real-time insights into almost every aspect of your business, from sales to customer service. But how do you get data to Azure in the first place? That's where an Extract, Transform, and Load (ETL) tool proves useful.

Salesforce is the world’s leading CRM (customer relationship management) software, with a 20 percent market share. The Salesforce CRM software is chock-full of features for business intelligence (BI) and analytics so that you can capture hidden insights and make smarter, data-driven decisions. The traditional ETL (extract, transform, load) process extracts data from one or more sources and then deposits it into a centralized data repository.

Choosing the right data warehouse is a critical component of your general data and analytic business needs. One of the biggest questions that businesses ask when choosing their data warehouse providers is this: Should you use Snowflake, Amazon RedShift, or Google's BigQuery data warehouse for your business needs? We've already covered Amazon RedShift vs. Snowflake and Google BigQuery vs. Snowflake, but what about Amazon RedShift vs. Google BigQuery?