The importance of big data cannot be understated as it allows businesses to make data-driven decisions, quickly adapt to the fluctuating market situation, and build new products that cater to users’ needs. Big data is the cornerstone of analytics techniques such as machine learning, natural language processing, deep learning, statistics, IoT, and more. But how can you store large amounts of data?
When we talk about big data, we imply high velocity and different types of structured and unstructured information as well as enormous amounts. In 2020, humans were generating 1.145 trillion megabytes of data each day. If you are struggling to comprehend this number, that is because it is incomprehensible. This is where technologies like data lake and data warehouse come into play. In this article, we will compare data lakes vs data warehouses to finally draw the distinction between the two. We will also touch on the best practices of selecting the right technology for your company.
A data warehouse facilitates the storage of structured and semi-structured data from various sources, including marketing, customer relationships, and sales. Both historical and current data is consolidated in a data warehouse allowing decision-makers to extract valuable insights for business intelligence activities. The main purpose of an enterprise data warehouse is to identify correlations between data derived from different sources.
You may think that this description sounds a lot like a database. If we compare data warehouses vs databases, the distinction will become apparent. While databases are mostly used to record information, data warehouses are utilized for data analytics. In other words, databases are useful for small transaction data for daily operations like new customer entries. Data warehouses, on the other hand, perform larger tasks such as data mining to discover unidentified insights.
Let’s take a closer look at data warehouses with a few examples:
Which industries take advantage of data warehousing technology?
Healthcare is an industry where a single mistake can lead to disastrous outcomes. In the medical sector, the relevance and timeliness of information can mean a difference between life and death. Data warehouses help healthcare workers make predictions, create treatment reports, and exchange data with insurance agencies, laboratories, and other researchers.
Especially in the finance and investment sectors, data warehouses play a major role due to significant amounts of money at stake. Even a single point difference can result in devastating financial losses for thousands of people. In this case, data warehouses are used to analyze customer behavior and market trends as well as other relevant data to make precise forecasts.
Retail chains can also take advantage of data warehouses for distribution and marketing purposes. You can track inventory, analyze pricing policies and promotions as well as closely examine customer purchasing behavior. All this information is crucial when it comes to business intelligence systems and marketing and sales strategies.
Let’s discuss the main benefits of building a data warehouse:
On top of storing information, big data data warehouses convert it into consistent formats that your analytics systems require. You get access to structured data that adheres to the predefined standards which enable efficient analytics activities. Moreover, when you access data, you receive historical backgrounds such as performance trends at the time and other key information that provides context to the data.
First of all, data warehouses relieve your data analyst of the duty of collecting data from different sources. Instead, they gain access to a system that gathers, structures, and stores all the relevant information and converts it into a standardized format. This freed-up capacity allows data analysts to devote more time to extracting business-relevant insights from this data, which increases the efficiency and quality of the analysis.
Scalability is vital for a growing business as more data and queries pile up that require structuring and analysis. Data warehouses provide solutions that enable greater business scalability and offer a competitive edge. On top of that, your enterprise data is kept safe and protected due to the increasing amount of security techniques used in data warehouses.
A data lake refers to a centralized location that stores enormous amounts of data in raw format. Unlike data warehouses, where data formats are standardized and information is structured and moved to different corresponding folders, a data lake is a large pool of data with object storage and a flat architecture. Each piece of data is assigned its unique identifier to streamline data retrieval. When comparing a data lake vs a data warehouse, the cost-efficiency of the former usually comes to mind. Due to the inexpensive object storage system and undefined formats, many companies can afford to use data lakes to store and retrieve information.
What industries will see the most benefit from data lake technology?
An increasing number of governments and non-profit organizations are moving towards smart cities with optimized waterways, improved law enforcement practices, enhanced education systems, and more. To achieve this goal, governments need to collect unthinkable amounts of data from vehicles and citizens. Data lakes provide solutions to this issue and allow smart city initiatives to move forward.
Although true to many sciences, genetics requires immense amounts of data to be taken to the next steps. Our bodies generate tons of information that can be used for new discoveries and identifying correlations. Data lakes allow scientists to collect as much data as needed to better understand the human genome and progress the field of genetics, making revolutionary improvements to our lives.
Nowadays, it is simply a matter of time before your company will be attacked online with scam emails, ransomware, DDoS attacks, or viruses. The only way to minimize the effects of cyberattacks is to be proactive as opposed to reactive. To stay alert at all times and be able to predict a possible hacker attack, you will need to collect large amounts of information. Even if you do get hacked, your data will be safely stored in a data lake which will dramatically reduce the consequences for your business.
Let’s take a look at a few main benefits of acquiring a data lake solution.
Data democratization refers to making information available across the organization. Instead of only a few executives having access to the company’s data, data lakes allow middle management and other employees to get insights and check reports. Decision-making can be very time-sensitive for a company and data lakes for big data provide users with information required to make a certain move.
As mentioned above, data lakes are substantially more affordable means of data storage as they do not require detailed architecture and predefined format standards. As a result, data lakes can indefinitely store raw data at a low cost and be used for machine learning and analytic purposes.
At the rate of technology innovation and development, you might need some currently irrelevant or unusable historical data in the future. When a new method or system becomes available, you will be able to dive into your pool of data and extract any necessary information. Data lakes provide enterprises with a cheap way of data storage that allows them to hoard all this information even if it will never be used.
Data warehouse and data lake definitions gave you an understanding of what these terms entail. Now let’s discuss the key differences between data lakes and data warehouses to figure out which system is more suitable for your organization.
Data lake vs data warehouse: table of comparison
Type of data
Structured, semi-structured and raw data from multiple sources
Structured data in quantitative metrics
Up to hundreds of petabytes (PB)
Mostly in terabytes (TB)
Storing any type of data for big data analytics, machine learning, deep learning, and more
Selected data for read-only queries and data summarization
Big data storage at a very low cost
Data analytics for business intelligence
Generally minimal security measures
High grade of data protection
Before we go over the best practices for selecting the right technology for your company, let’s take a look at the most prominent data warehouse and data lake vendors:
Amazon Redshift is the most popular and renowned data warehouse provider on the market. Fortune 500 enterprises utilize this vendor, including McDonald’s, Yelp, Foursquare, Pinterest, and many more. Amazon Redshift allows users to move and store large amounts of structured and semi-structured data from multiple company’s sources and use it for analytic purposes.
Snowflake is another prime data warehousing solution that is trusted by companies like JetBlue, DoorDash, and Western Union. Snowflake offers a flexible pricing structure that allows users to pay as they go. The per-second pricing model helps businesses cut costs without jeopardizing the quality.
AWS Data Lake is an Amazon data lake vendor popular among big players like Netflix, Zillow, and NASDAQ. The tool utilizes AWS services to search, tag, share and analyze data within the company. Users gain access to the data lake to locate specific datasets and use them for their business decisions.
Cloudera is one of the most renowned data lake solutions whose client list includes enterprises like Cisco, Dell, and eBay. The platform allows managing and scaling petabytes of data as well as thousands of users. You can access and govern datasets from one consolidated place and utilize them for your business needs.
How do you choose which type of technology is most suitable for your company’s goals? We have prepared a brief overview of the best practices that can help you make the decision.
An organization that regularly uses just a few data sources might not largely benefit from implementing data lake technology. However, if you are attempting to collect lots of data from various sources, you could take advantage of a data lake solution.
Who will be using the technology? As mentioned in the comparison table, data lakes are mostly employed by data scientists whereas data warehouses are useful for data analysts. If you are not planning on running various tests with your datasets and applying the hoarded data for machine learning and other analytics technologies, having a data lake solution might be redundant.
If your data seems to be broken, incomplete, missing, or inaccurate, building a data warehouse or data lake will not benefit your business. Both solutions require data observability which means that you are able to evaluate the health of your data. To achieve data observability, you need to work on your data governance and quality standards and practices.
The amount of data will continue to grow and expand in the coming years which amplifies the reliance on cloud storage providers. The inevitable increase in data volumes, including operational, streaming, observational and other data, puts big data storage capacity at stake. While traditional data storage systems fail to process terabytes and even petabytes of information, cloud storage solutions offer the needed scalability.
Cloud data lakes and data warehouses are capable of solving the capacity issue and helping businesses achieve real-time data objectives. Leveraging the newly acquired scalability and flexible pricing structure allows companies to access relevant data in real-time and make smart data-driven decisions. To learn more about data storage systems and how they can benefit your business, get in touch with NIX United. We are a team of software engineers with extensive knowledge of business intelligence (BI) solutions. Contact us to get insights about leveraging BI services to uncover growth potential.
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