Big Data Data Analytics Data Modeling

Data Lake vs. Data Warehouse: What’s the Difference?

What is the Difference Between a Data Lake and Data Warehouse?

To begin, the two offer similar functions for business reporting and analysis. But they have different use cases depending on the needs of your organization. 

A data lake acts as a pool, storing massive amounts of data kept in a raw state. This can be used to store structured, semi-structured, and unstructured data from a variety of sources such as IoT devices, mobile apps, social media channels, and website activity. 

A data warehouse, on the other hand, is more structured unifying data from multiple sources that have already been cleansed through an ETL process prior to entry. Data warehouses pull data from sources such as transactional systems, line of business apps, and other operational databases. Another principal difference between the two is how each makes use of schema. A data warehouse utilizes a schema-on-write, while a data lake makes use of schema-on-read. 

When it comes to users, a data warehouse is typically used by a broader range of roles such as business analysts using curated data, along with data scientists and developers who focus on driving insights from the raw data to obtain more customized results.

Who Benefits From Each Type? 

Depending on your organization, you can actually benefit from both types of data storage solutions. A combination of one or both can benefit your business depending on your data stack and requirements for data analysis and reporting. 

Historically, data lakes are used with companies that have a dedicated support team to create, customize, and maintain the data lake. The time and resources needed to create the data lake can be extensive, but there is also a wide selection of open source technologies available to expedite the process. If you need to handle large amounts of raw data as well as flexibility, this may be a good solution for you. 

If you need a solution that’s ready to go, a data warehouse platform provides you with a structured setup that can be a good option for analytics teams. Data warehouses typically cost more than data lakes, particularly if the warehouse needs to be designed and engineered from the ground up. Though AI-powered tools and platforms can drastically advance the building timeline and minimize expenses, some companies still take the in-house approach. Overall, data warehouses can be vital to companies that need a centralized location for data from disparate sources and accessible ad-hoc reporting.

Why Should You Use a Data Lake or Data Warehouse? 

Advanced tools make data warehouse design simple to set up and get started. These are typically offered as an integrated and managed data solution with pre-selected features and support. These can be a great option for a data analytics team due to their quick querying features and flexible access. If you need a solution that offers a robust support system for data-driven insights, a data warehouse may be right for you. 

If you prefer a quicker DIY method, a data lake might be a better solution. Data lakes can be customized at all levels such as the storage, metadata, and computing technologies based on the needs of your business. This can be helpful if your data team needs a customized solution, along with the support of data engineers to fine-tune and support it. 

What Should Be Considered When Selecting a Solution? 

At the end of the day, your business may need one or both of these solutions in order to gain high-level visibility across your operations. This holistic approach has led to the development of newer solutions that combine the vital features of both. The data lake house takes advantage of the more common data analytical tools along with added agents such as machine learning. 

Another factor to consider is the amount of support that your analytic teams currently have. A data lake typically needs a dedicated team of data engineers, which may not be possible in a smaller organization, but as time goes on, data lake solutions are becoming more user-friendly and require less support. 

Before selecting one of the two, take a look at who your core users will be. You should also consider the data goals of your company to understand the current and future analytics needs. What may work for one company may not work for yours, and by taking a closer look, you can find a data solution that best meets the needs of your business.

Data Analytics Data Modeling

Disparate Data: The Silent Business Killer

Data can end up in disparate spots for a variety of reasons. Deliberate actions can be taken in the interest of not leaving all your eggs in one basket. Some organizations end up in a sort of data drift, rolling out servers and databases for different projects until each bit of data is its own island in a massive archipelago.

Regardless of how things got this way at your operation, there are a number of dangers and challenges to this sort of setup. Let’s take a look at why disparate data can end up being a business killer.

Multiple Points of Failure

At first blush, this can seem like a pro. The reality, however, is that cloud computing and cluster servers have made it possible to keep your data in a single pool while not leaving it subject to multiple points of failure.

Leaving your data in disparate servers poses a number of problems. First, there’s a risk that the failure of any one system might wipe information out for good. Second, it can be difficult to collect data from all of the available sources unless you have them accurately mapped out. Finally, you may end up with idle resources operating and wasting energy long after they’ve outlived their utility.

It’s best to get everything onto a single system. If you want some degree of failure tolerance beyond using clouds or clusters, you can set up a separate archive to store data at specific stages of projects. Once your systems are brought up to speed, you’ll also begin to see significant cost savings as old or excess servers go offline.


With data spread out across multiple systems, there’s a real risk that things won’t be properly synchronized. At best this ends up being inefficient. At worst it may lead to errors getting into your finished work products. For example, an older dataset from the wrong server might end up used by your analytics packages. Without the right checks in place, the data could be analyzed and out into reports, producing flawed business intelligence and decision-making.

Likewise, disparate data can lead to inconsistency in situations where multiple teams are working. One group may have its own datasets that don’t line up with what another team is using. By centralizing your efforts, you can guarantee that all teams will be working with the same data.

Bear in mind that inconsistency can get very far out of hand. If you need to audit data for legal purposes, for example, you may find data that has been retained too long, poorly anonymized or misused. With everything centralized, you’ll have a better chance of catching such problems before they create trouble.

Security Risks

More systems means more targets. That opens you up to more potential spots where hackers might get their hands on sensitive data. Similarly, you’re stuck with the challenge of patching multiple servers when exploits are found. In the worst scenario, you may not even notice a breach because you’re trying to juggle too many balls at the same time. Simply put, it’s a lot of work just to end up doing things the wrong way.

Turf Wars and Company Culture

When different departments in control of different data silos, it’s likely that different groups will start to see the data within their control as privileged. It’s rare that such an attitude is beneficial in a company that’s trying to develop a data-centric culture. Although you’ll want access to be limited to appropriate parties, there’s a big difference between doing that in a structured and well-administrated manner versus having it as the de facto reality of a fractured infrastructure.

Depending on how culturally far apart the departments in a company are, these clashes in culture can create major friction. One department may have an entirely different set of security tools. This can make it difficult to get threat monitor onto a single, network-wide system that protects everyone.

Conflicts between interfaces can also make it difficult for folks to share. By building a single data pool, you can ensure greater interoperability between departments.


Consolidating your data systems allows you to create a tighter and more efficient operation. Security can be improved rapidly, and monitoring of a single collection of data will allow you to devote more resources to the task. A unified data pool can also foster the right culture in a company. It takes an investment of time and effort to get the disparate data systems under control, but the payoff is worth it.

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Data Modeling Data Preparation

The Marketing Analytics Tool You Need in 2019

The Purpose of Marketing Analytics Tools

The field of marketing is a very large, intense, and sometimes complicated web of customer data from a wide variety of sources. Not only do you have the umbrella categories of marketing tools such as CRMs, paid ad managers, social media, website analytics, etc., you also have the numerous tools that fall under each of those categories, leaving you with upwards of 10 data sources to attempt to collectively analyze without spending a week’s (maybe even a month’s) worth of time creating unattractive an unreliable pie charts on Excel that need to be updated everyday. Marketing analytics can be difficult to conquer…unless you have the right knowledge and marketing analytics tool.

Isn’t my CRM the Marketing Analytics Tool I Need?

Thankfully, CRMs such as HubSpot and SalesForce are very talented at keeping their data organized properly, and HubSpot has decent reporting tools. The problem is that these marketing analytics tools report only their data. Yes, some CRM’s, such as HubSpot, let you integrate your Facebook ads and Google analytics, but they are not included in their reporting tools when it comes to comparing marketing emails to social media ad performance to website website traffic… see what I mean yet?

Advertising Data

Paid ads managers and social media, such as Google, Facebook, LinkedIn, Twitter, YouTube, etc., each have their own reporting and marketing analytics tools, and while some of them are decently detailed, some of them also – for lack of a better phrase – totally suck! Not only that, but you’re limited to only analyzing their data. So, what if you want to know if your YouTube video views spiked when you ran your new Twitter campaign? What if you want to know if your LinkedIn profile engagement decreased because of a low quality Facebook campaign? Sure, go ahead and bounce around from website to website… go ahead and waste an immense amount of time.

Website Data…Yikes!

Your company’s website data is the kicker, and what will ultimately prove my point. Not only are you interested in your website visits, specific page visits, traffic sources, and about 35 other things, but you’re also A/B testing your landing pages, wondering why your pricing page has a 80% drop off rate, and why half of your visits are from people who live in a country you’ve never heard of. My point is, every business has a wide variety of numerous questions about their website traffic and conversions, or lack of. Is it being affected by your email campaigns? Or by your Facebook and LinkedIn ads? Or by your YouTube videos? Or by your unknowingly low quality landing pages? There are so many questions, and even more data sources. Utilizing the right marketing analytics tool along with a pinch of automation is the key to answering your vast list of questions about your website traffic behavior.

How Do I Combine All of This Data?!

The solution? An end-to-end marketing analytics tool to collect your data from each and every source, in real time, modeling it into a single dashboard that can virtually answer any question you have about your marketing and customer data. Given the opportunity to pull any single piece of data and compare to another single piece of data, your questions and answers about what you are doing right – and more importantly, what you are doing wrong – are endless. Make the choice to stop wasting your time and money on bad marketing decisions and analyze your data with immense precision and speed, using Inzata. Compare Hubspot to Facebook to YouTube to Google to MailChimp to Salesforce to Twitter to LinkedIn to any source you can think of, in real time, with ease. Skyrocket your marketing team’s performance with Inzata. 

Data Modeling Data Preparation

Data Lake? More Like Data Swamp!

Building a collection of data sources that a business or an organization has into a data lake that everyone can access is an idea that inspires a lot of interest. The idea is to make the data lake into a resource that will drive innovation and insights by allowing clever team members to test ideas across many sources and variables. Unfortunately, a lack of good data curation techniques can lead that lake to become a data swamp in no time at all.

An Example

Let’s say you want to take a database that contains information about all the employees at your company. There are two data sources, with one that includes an employee’s name, salary, birthday and current address. Perhaps the second source includes information about their name, current city of residence, listed hobbies from their application and salary.

You want to bring these collections of information together. That’s the data ingestion process. Data_Lake_721_420_80_s_c1

There will be transformation needs, as you’ll have to breakdown information like the address into its constituent pieces, such as street, city, state and ZIP code. Similarly, the street address itself may be one or two lines long, depending on things like whether there’s an apartment number or a separate P.O. box. There may also be more advanced issues, such as differences in formatting across countries.

Schema issues also present problems. For example, let’s say you have an entry in your first source for “John Jones” and another for “John J. Jones” or something similar. How do you decide what constitutes a match? More importantly, what criteria can be used to ensure actual matches are obtained through the kinds of automated processes that are common during data ingestion?

In the best-case scenario, good data curation practices are in place from the start. Some sort of unique identifier is employed across all your data tables that matches people based on, for example, employee ID numbers that are never reused. In the worst-case scenario, you simply have a bunch of mush that’s going to have to be stabbed at in the dark.

The Role of Human Curation

Even if your organization employs best practices, such as unique IDs for entries, date stamps and preservable identifiers across transforms, there are going to be curation needs in virtually every data set. Perhaps you get super lucky and all the data lines up perfectly based on those ID tags, too. Many other things can go wrong.

For example, what happens if there’s a scrubbed or foreign character in an entry? For example, HTML entities are often transformed by security protocols prior to database insertion to prevent SQL injection attacks.

Data sources can also induce problems. Perhaps you’ve been importing information from a CSV file, and you don’t notice one or two entries that throw the alignment off by one or two columns. Worse, instead of getting a runtime error from your code or your analytics package, it all appears to be good. Without a person scanning through the data, you won’t notice a flaw until someone pulls one of the broken entries. In the absolutely worst scenario, critical computational data ends up being passed along and ends up producing a flawed work product.

Providing Access

Okay, you’ve gotten all that business straightened out. Curation superstar that you are, everything aligns beautifully, automated processes flag issues and humans are double-checking everything. Now you have to put usable information into your employees’ hands.

First, you need to know the technical limits of everyone you employ. If someone can’t code an SQL entry, you need to have data in additional formats, such as spreadsheets, that will allow them to load it into their own analytics packages. Will you walk back those transforms in the output process? If so, how do you confirm they will be accurate renderings of the original input?

Likewise, the data needs to be highly browseable. This means ensuring that servers are accessible, and they also need to contain folders with structures and names that make sense. For example, the top level folders in a system may place an emphasis on generalizing their contents, such as naming them “employees” and “customers” for easier reading.

Data curation is a larger cultural choice for an organization. By placing an emphasis on structure the whole way from ingestion to deployment, you can ensure that everyone has access and quickly begin deriving insights from your data lake.

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Guide to Cleaning Data with Excel & Google Sheets Book Cover by Inzata COO Christopher Rafter