Data Warehouse – Should You Get One?

Maybe you are dependent on spreadsheets and data silos when you make business decisions, or perhaps you would rather have standard formats and structured data as you make sense of information. Moreover, you may be dealing with data inconsistencies or access restrictions to specific records. If this is the case, it may be time to consider having a data warehouse.

Most businesses, may they be start-up ventures or long-standing companies, have started to look into data warehousing as a solution to their need for significantly improved data analytics for their operations. Some have been using financial spreadsheets via Excel or maybe Google Analytics or Mixpanel for some website testing. They have grown accustomed to accessing separate locations for these tools, and it has worked for many years.  

However, with increasingly growing data, there may be a need to have one storage for all this information. But to accomplish such seems like an arduous task. If there is a quick and seamless way to do it, it may be time to consider data warehousing.

Data Warehouse at a Glance

Over the years, countless applications and software have been developed to address the need for data storage for businesses that accumulate thousands (maybe millions) of daily information from their transactions. Apart from having a repository for all this information, it’s vital to have available options to manage data, update, delete, or retrieve them as necessary.

Many online transaction processing systems (OLTP) have been created to address this need, and relational and non-relational databases include DB2, MySQL, Oracle, SQL Server and Redis, MongoDB, and Cassandra, respectively. There are also ones like Hadoop, which are considered file systems for those looking for raw data storage alternatives.

As opposed to OLTP, data warehouses are considered online analytical processing systems (OLAP), which is a way to safe keep data so that it can go through an extraction, transformation, and loading on to other operational source systems. Models may also be employed using this information for data analytics and to generate reports.

Types of data warehouses

Although various types of data warehouses have surfaced over the past years, there are three typical ones.

Data mart

It’s a smaller version of the more significant data warehouses and is meant to support a particular division, region, or small business unit.

Data store

This data store is as large as the typical data warehouse. Still, it is more focused on operational reporting as information is updated on a real-time basis for easy retrieval and access.

Data warehouse

It is the most typical of data warehouses and is created as a central repository for all information used for the entire business in all decision-making.

Why should you have a Data Warehouse?

Data integration 

As is true for most large corporations, data will come from multiple sources and you integrate them as a tool for both operational and strategic decision-making. Otherwise, your basis for critical judgments may be too segmented or scattered to be optimal.

For instance, you may need crucial details, weekly, on your most valued customers, such as their payment information and their historical buying activities, which may be located in different areas in your company. If you want to track this particular subset of your client base, it will be of great help if you can access them via centralized storage instead of going through a couple of separate locations and then having to pool them together. 

Data querying

Your source of data may not be readily available for constant querying.  For data analysts to effectively work through essential information, they must have easy access to all the data they need. For example, most business intelligence (BI) tools are incompatible with NoSQL data storage systems like MongoDB. As such, all information kept in custody in the backend must be transported onto a data warehouse before it can be made available to analysts.

When transactions come in droves, and you deal with hundreds, if not thousands, of information in the daily conduct of your business, it would be best if you have a system to collate your data together and convert them into forms that are open for queries. Otherwise, it will take ages to gain access to pertinent data that you may need at a whim’s notice. Keeping them in their raw format will also put a burden on your database unnecessarily.

Data categorization

With a data warehouse, you will have the ability to categorize data into transactional and analytical ones. Your activity logs that exist on mobile applications and other small bits of information must not be accessed directly by analysts for them to work on the data. They must be processed and transported someplace else to avoid having to tamper with the transactions database. 

Having a data warehouse will give you a location where everything flows for data analysis. It can allow advanced queries and a place where data stops for further processing. When daily accumulated information is kept from analytical data, your app’s performance remains undisturbed.

Data analytics

Much of the decision-making done by business leaders today involves developing strategies and planning based on facts and figures made available as data sets, already processed to provide valuable insights. These must be readily accessed to stay ahead in the game, considering that other companies are investing in data analytics to be highly competitive.

Advanced data analytics will be significantly enhanced if a data warehouse is present to collate all information and process them to generate valuable reports. Historical information may also be provided to evaluate which programs and initiatives in the past worked well for the company and which ones didn’t to be guided on necessary steps moving forward. Business executives need such data to make adjustments on their strategies on costs, sales, and improving the company’s overall financials. 

Data Warehouse options

It’s not very difficult to sift through options for data warehouses today as most are quite useful in providing data storage solutions.

The most widely-used and popular cloud-based providers are Azure by Microsoft, Amazon Web Services, and Google Cloud. Feel free to do your research on all three, but your decision may likely be influenced by which platform you are currently using. It will be much easier to go with the data warehousing option for your existing platform unless there’s a pressing reason why one from another will work better for your business.

For instance, if you have Google Analytics and you want to export your data onto your warehouse seamlessly, then you will need BigQuery by Google Cloud for that. For Amazon, Redshift is the best option, and for Microsoft Azure, there is the SQL Data Warehouse.

All of these work well with most BI tools, so setup is straightforward. They are reasonably priced since they are on a pay as you use basis, compared to old school data warehouses where you have to make substantial investments, shelling out thousands of dollars, before you can even use your storage. Lastly, modern data warehouses are quite sophisticated and robust, brandishing the massively parallel processing systems (MPP) powerful enough for databases. 

Looking at alternatives 

Are there alternative options apart from the three above-mentioned cloud-based solutions? Because of modern data sources such as audio, video, logs, IoT, and social media, there has been an influx of substantial changes in volume and content of data warehouses. Consequently, new solutions must be provided for some business issues that have cropped up, and other data structures that need to be adjusted.

It has been a challenge to adhere to data governance principles, especially in matters of data ownership due to the lack of check-points on some data warehousing initiatives and new methods in data storage are in order.

Therefore, emerging technologies came out like polyglot persistence, non-relational databases, data virtualization, and data lakes.

Non-relational SQL

NoSQL is designed architecturally to bring a unique approach to databases. A typical relational system will provide an organization with a wealth of information via rows, columns, tables, and other schemas for CRUD operations (create, read, update, and delete). On the other hand, a NoSQL database will not have such relational structures but models that are quite flexible, scalable, and quickly accessed. These features are the most important ones when dealing with Big Data.  

NoSQL databases present in the market today are as follows:

Graph database

Countless entities are represented based on query relationships, maps, and storage through graphical structures. OrientDB, New4J, InfiniteGraph, and Sparksee are some of the more famous providers in this category.

Wide-column database

This type is similar to the traditional database utilizing tables, columns, and rows, but the formatting and labels may vary from cell to cell. Data that are related to each other are categorized in one location for easy recoveries in queries. A single query is all it takes to gain access to grouped data instead of multiple retrievals in relational databases.

Hypertable, DynamoDB by Amazon, HBase, Cassandra, Cloudera, and Hadoop are among the databases of this type. 

Document database

Azure’s DocumentDB, RavenDB, Terrastore, CouchDB, MongoDB, and Elastic are decent document stores that you can choose from to keep documents in multiple formats.

Key-value database

Data is represented as blobs in this category of databases, and a key provided is the only way to gain access to the value in one collection of information. Redis, Riak, Table Storage by Azure, and DynamoDB are the offerings for this type. 

Data Lakes

Data lakes as an alternative to data warehouses are excellent storage for unstructured data and kept for an indefinite period for some future use.  This way, analysts are free to explore and develop models as may be necessary to come up with innovative business solutions. All data types in this repository are supported, extracted from a source system, and all modeling and processing may be done for any analytical requirement.