The Data Warehouse Dilemma: Too Much Data, Too Little Insight?

Data warehouses are supposed to keep important business data in one centralized place where all employees can access it to provide insight into critical business decisions.

But what happens when there’s so much data that nobody knows what to do with it? Businesses need to ensure they have the right data infrastructure in place, or they’ll find their data warehouses become more of a problem than an asset! Here’s how to avoid creating too much data and not enough insights.

Let’s start with basics

One of the most discussed aspects of enterprise information management today is what we call the data warehouse dilemma.

Basically, there is a mismatch between the volumes of data that companies need to process and store and the capabilities that they have to do so.

So much data has been created in recent years — and at an increasing rate each year — that organizations are having a difficult time reconciling this information with their daily operations.

At this point, many firms are turning to outsourced solutions or outside vendors for help dealing with the complexity involved in data analytics and extraction for purposes such as customer service.

Difference between a database and warehouse

A database is not a data warehouse. What are the differences between a database and warehouses or data warehouses? Data warehouses differ from databases in two fundamental ways.

First, the storage techniques that are used for the two types of systems can vary significantly. For example, most databases store their data in tables with rows and columns that look like this:

Name1 John Smith , Address1 123 Main Street, City New York, State NY, Zip 1234–5678.Name2 Mary Jane Smith, Address2 234 Main Street, City New York State NY ZIP 1234–5678

However, a data warehouse will have columns that are labeled by subject matter instead of name and address.

Problems with DBMS

Data warehouses are only as effective as the way in with which they’re managed.

Data warehouse management systems and processes need to be designed so that data warehouse users can efficiently access and use the right data at the right time.

Consider what type of search function you might want your application to have in order to retrieve data quickly and easily.

Also, think about how much work or system downtime it would take for a user to do this on their own through SQL queries and extract, transform, load (ETL) tools rather than using your software.

Ideally, your application should offer a customizable query builder with predefined searches so that end users don’t have to know SQL syntax in order to find specific information.

Problems with NoSQL architecture

- NoSL databases have many different types of data. There is no indexing system and search is not automated, meaning that it can take hours to find a piece of information. This means less data visibility and analytical insight.

- One of the most common use cases for NoSQL databases is storing social media data; but in order to do this properly you need access to an API. Without knowledge about how the API works or any way to debug queries, there is often no way to extract data from these databases.

- The lack of sophistication in SQL doesn’t help either. For example, analysts may want to run complicated queries on relational databases without having to break them down into multiple steps or even write multiple queries;

- These database are also open source which means they are vulnerable.

What is Spark?

There are many different tools in the Hadoop ecosystem to assist with data processing. In this post, we’ll be taking a look at Spark.

Compared to MapReduce which is most often used for batch-based processing jobs, Spark is able to handle both batch and streaming tasks in memory for fast real-time analysis.

One of the areas that Hadoop does not perform well on is queries over structured or semi-structured data such as SQL databases.

Spark can solve this problem by using its Catalyst component which allows programmers to write their own programs called User Defined Functions (UDFs) or algorithms in Scala or Python.

Where does Hadoop fit in?

Since Hadoop is a data storage system, it makes sense that many companies are starting to utilize it to manage their data. However, there are some disadvantages and limitations with this solution.

Hadoop may be the right tool for certain organizations with specific needs and goals in mind. A good example of this would be if your organization has massive amounts of unstructured data or doesn’t have immediate access to IT support.

By understanding the ways in which Hadoop can provide a solution for your organization’s unique needs and challenges, you can see whether or not it would work for you! In other words, like most technology solutions, Hadoop is better suited for some scenarios than others.

It just depends on what you’re looking to get out of your data warehouse and how much time and effort you want to put into achieving those results.

Questions to ask before building a data warehouse

1. Is your business data segmented at a high enough level to create distinct groups of customers or regions that generate significantly different revenue streams?
2. 2. What is the current state of your business analytics?
3. 3. Do you have clearly defined KPIs that could help inform the design process (e.g., lead conversion rate)?
4. 4. How much raw data do you generate in a given time period and how frequently does it change (e.g., number of weekly customers, number of products sold)
5. 5. Does anyone in your organization work with data on a day-to-day basis for reporting purposes?

Verdict

We all know that data storage is cheaper and easier than ever before. Organizations generate terabytes of data on a daily basis, yet struggle to make sense of it. What are the best practices for optimizing a data warehouse in the age of big data? Let’s discuss.

1. No more mystery meat. Before you start, take inventory of what you already have.
2. If your organization generates terabytes of data every day but has no clue what information they already have, you’re going to end up creating something new rather than fixing something old- which isn’t going to help anything at all.