6 Things to Think About Before Considering a Data Warehouse

If you are thinking to go for a data warehousing consulting management system. Consider these factors when determining which data warehouse can better meet the company’s needs.   1. Various types of data You will … Read More

If you are thinking to go for a data warehousing consulting management system. Consider these factors when determining which data warehouse can better meet the company’s needs.


1. Various types of data

You will want to store three types of data for your business: structured, unstructured, and semi-structured data. Most data warehouses can handle structured and semi-structured data, but unstructured data is best suited for data lakes.


  • Structured data is quantifiable information that can be neatly sorted into rows and columns (e.g., sales records or customer contacts).
  • Unstructured data is data that is difficult to process and interpret. Consider written material (such as blog posts or responses to open-ended survey questions), photographs, videos, audio files, and PDFs. If you just want to store unstructured data, a data lake is a better option than a data center.
  • Semi-organized data is a mixture of structured and unstructured information. Take, for example, an email. The email’s content is unstructured, but there are quantifiable dimensions of it, such as who sent it, where they sent it when it was opened, and so on.

Similarly, an image is unstructured in and of itself, but you also have access to organized data such as the time the photo was taken, system model, photo scale, geotags, and so on.


If semi-structured data is essential to you, BigQuery and Snowflake are two data warehouses known for providing the best architecture to support semi-structured data management and queries.


2. Data storage scalability

Most data warehouses allow you to store vast volumes of data without incurring significant overhead costs. You won’t need anything more than what they have, particularly if analytics is your primary use case.


However, you should care about how a single warehouse scales data capacity during peak hours. When you need more resources and processing space, Amazon Redshift, for example, would enable you to manually add additional nodes (the simple systems of data warehousing that contain data and perform queries). Snowflake, on the other hand, provides an auto-scale feature that dynamically adds and removes clusters of nodes as desired.


3. Performance scalability

The output of a data warehouse refers to how quickly the queries can run and how quickly you can sustain that pace in periods of heavy demand. As one would expect, scaling for output and data storage are inextricably linked. Output, like storage, can improve as the number of nodes in your warehouse grows.


Nowadays, the pace is unimportant. Any warehouse is roughly as fast as the others. What you really want to think about in terms of success is how much leverage you want over your pace.

You can connect and delete nodes for quicker queries in the same manner as a data warehouse’s storage scales. Some warehouses, such as Redshift, need this to be done manually, but you will be able to tune it as precisely as you want. Others, such as Snowflake, will do so automatically for a hands-off experience.


4. Upkeep

You probably want your engineers to be concentrating on constructing and managing your goods rather than thinking about ETL pipelines and day-to-day warehouse management, particularly if you have a small team. In that case, you’ll want a self-optimizing data warehouse like BigQuery, Snowflake, or IBM Db2.

However, by manually running the facility, professional data warehouse architects can gain more power and consistency in optimizing it specifically for your company’s needs. Redshift and PostgreSQL are your best choices if you want this degree of control over the efficiency and expense of your warehouse.


5. The Ecosystem

Consider using a data warehouse that is integrated with the environment of the software you currently use. For eg, Azure Synapse Analytics is part of the Microsoft product ecosystem, Redshift is part of the AWS ecosystem, and BigQuery is part of the Google Cloud ecosystem. Since you already have an architecture in place, this would make deployment easier.

Otherwise, the engineers would need to build several custom ETL pipelines to get the data where it needs to go. You will also need to write a custom ETL to bring data into your warehouse from specific data sources, but the aim is to reduce the amount of work you have to do.


6. Price

Space, warehouse capacity, run time, and requests are all variables in data warehouse pricing. Redshift charges every hour depending on nodes or bytes scanned. BigQuery, on the other hand, offers both a flat-rate and a per-query pricing model. Snowflake, IBM Db2, and Azure are both discs and compute time-based utilities.

Finally, you want to pick a data warehouse that can do what you need it to do, not the cheapest one.


PostgreSQL is a perfectly free choice for businesses with a small budget but also has a lot of features. When you’re ready to update, switching data warehouses is quick, particularly if you’re using a consumer data platform like Segment that can interact seamlessly between the two warehouses.

Author: admin