What are the best data types and formats for your data warehouse model?

Here are the steps to implement a robust data warehouse model: 1.Use appropriate types like integer, decimal, varchar, and date to optimize storage and query performance. 2.Standardized formats such as Parquet or ORC for efficient storage and query processing. 3.Ensure data quality through validation, cleansing, and enrichment processes. 4.Implement role-based access controls, encryption at rest, and in-transit to safeguard data. 5.Deploy dimensional modeling for analytical queries and star or snowflake schemas for flexibility. If on AWS; leverage Redshift, use features like automatic compression, columnar storage, and Redshift Spectrum for querying external data sources, S3 Object Storage to enhance data warehousing capabilities.

Choosing efficient data types is important, but in the ever-changing landscape of data, periodic reassessment is key. This ensures alignment with evolving analytical needs and ensures adaptability for changing requirements and insights.

Keep the number of data types limited to draw maximum benefits in terms of code management, reduce complexity. The following four types are recommended: Integer, decimal, timestamp and character(n). If there is specific need for using other data types then it must be approved by the stakeholders and architects after providing proper justification to use those.

Couple of best practices for data types which are very simple yet effective: 1. Allocate the correct data type to inputs; store numerical values like 25 as a number data type, not as "25" in VARCHAR to avoid resource-heavy string comparisons. 2. For columns with a maximum value length of 10 characters, avoid using VARCHAR(4000) unnecessarily Ideal data formats: 1. For petabyte-scale warehouses, Parquet with Snappy compression is ideal for fast data access. If saving costs is crucial, opt for GZIP compression for better storage optimization. 2. Snappy allows faster decompression during reads than GZIP because it supports parallel splits in data decompression, which GZIP does not offer. However, GZIP is more effective in reducing file size.

Choosing the data types is key as more and more distributed computing we do data distribution and consolidation will differ if we don't assign the appropriate format, example if the data is spread across periods which is in date format we can create pseudo integer column on top of that for effective retrieval and distribution .

It seems CSV ,JSON ,XML and Parquet are the extensively used data formats in any project. 1.CSV demands less technical skills and can be accessed using most applications. Faster to handle, smaller in size but has poor support of special characters. 2. JSON is fairly easy to RW. However can't be readily streamed and will need to break it up into individual objects. 3. If trying to store hierarchical data it would definitely be easier in XML over CSV. However it has complex syntax and performance is real slow. 4. Parquet is good for storing and reading of big data of any kind in less time as it is columnar storage. However, it's not useful if you want to read whole records for processing or you want to restructure and modify the schema often.

Parquet has been my choice of Data Storing Format for years now. It's columnar storage helps to compress the data size significantly. It retains metadata. It has high read speeds. I would choose Parquet over CSV, Avro, JSON, etc. It is amazing for small to medium size datasets, since running a DML action would mean practically: read, delete the source file, and rewrite a new file. A Delta Lake table stores data in Parquet files while addressing the above issues. Unfortunately, not all query engines have Delta Lake drivers available.

If we go by the definition of a data warehouse, the ideal option to work with would be columnar file formats such as parquet, ORC or delta. These file formats provide the optimal features for analytical workloads and often provide better query performance as compared to traditional/familiar flat file formats such as CSV, JSON, etc. They are also optimized to be memory efficient (thanks to compression) so apart from it being highly efficient in performance, it becomes even imperative for cost savings especially when big data is in the picture (which usually IS the case in this day and age).

Parquet and csv are easy to handle in structured data. JSON and ARRAY for semi structured data. Parquet is a columnar storage file format optimized for big data processing frameworks like Apache Hadoop and Apache Spark. It offers efficient compression and columnar storage, making it well-suited for analytical workloads. Parquet files are commonly used in data lakes and data warehousing environments.

Every data model type has its pros and cons. Star schema gives faster reads, but comes with the trade off of data redundancy. Snowflake schema, addresses this redundancy but leads to complex joins. Both the models, however, are required to be built in a go; any changes to data impact the whole model. Data Vault gives the flexibility of building the data model in a truly agile way. Figure out the business keys, and other data elements can vary for each source. It also gives a sense of true business keys, not just technical ones. Parallel loads of data in the hubs and satellite tables enables faster data load.

Traditional methodologies such as Third-Normal Form (3NF) and Star Schema have inherent complexities and fixed structures in the data modeling domain. They often find the process of adapting to rapid business changes rigid. On the other hand, Star Schema struggles with reengineering for changes in granularity. 3NF, with its complex primary keys and snapshot dependencies, could get complicated. Data Vault emerges as the runaway best in being highly adaptable and agile. This data architecture, designed specifically to fit the rapid pace of business changes, is a flexible and scalable one, faster than traditional models for accommodating and enabling data agility. That been said moving from Star Scheme to Data Vault might be costly.

Ah, data quality in a data warehouse model, that's the secret sauce that makes your data dishes taste exquisite! Just as the quality of your ingredients can make or break your dish, the quality of your data can make or break your insights. So, what makes for top-notch data quality? It's all about the CRISP-ness of your data - and no, I'm not talking about lettuce. It's about ensuring your data is Consistent, Reliable, Integrated, Standardized, and Precise.

Ensuring data quality is crucial for a robust data warehouse model. Define clear standards and expectations, including data definitions and rules. Automate processes like profiling and cleansing to maintain consistency. Regularly review and report on data quality issues to identify trends and anomalies. Collaborate with stakeholders and users to align expectations and address concerns effectively. Consistent efforts in these areas will help maintain high-quality data for informed decision-making.

Data security is paramount for any data warehouse model. Start by identifying and classifying risks related to data sensitivity and compliance. Enforce robust policies such as encryption and access controls to safeguard data. Regularly monitor and audit activities to detect and mitigate security threats. Educate staff and users to promote a culture of security awareness and compliance. By implementing these measures, you can protect your data assets and maintain trust with stakeholders.