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?

Data warehouses help you run logical queries, build accurate forecasting models, and identify impactful trends throughout your organization. But, what goes into designing a data warehouse? Whether you choose to utilize a pre-built vendor solution or you're starting from scratch — you'll need some level of warehouse design to successfully adopt a new data warehouse.

Snowflake is a robust data warehouse that has changed the data science game for many organizations. Snowflake lets you analyze your data using the most sophisticated query engine available today with its cloud-native architecture. But using Snowflake is not always as simple as using other products on the market. Below are nine expert tips to help you master the Snowflake platform.

Data teams across companies have continuous challenges of consolidating data, processing it and making it useful. They deal with challenges such as a mixture of multiple ETL jobs, long ETL windows capacity-bound on-premise data warehouses and ever-increasing demands from users. They also need to make sure that the downstream requirements of ML, reporting and analytics are met with the data processing.