Storing data is a vital aspect of modern businesses, especially those that rely on data analysis and science to make informed decisions. Effective data storage allows companies to collect, manage, and analyse large amounts of information gathered from various sources, providing useful insights that can facilitate business growth and innovation. However, selecting the most suitable data storage solution can be a daunting task due to the many available options. This article will compare four popular data storage solutions: data warehouses, data lakes, Delta Lake, and lake houses. It will examine their crucial features, common applications, and advantages/disadvantages, primarily focusing on their appropriateness for data science and analysis. By the end of this article, readers will have a better understanding of which data storage solution is appropriate for their business's data science and analysis necessities.
In today's world, the term "data" is ubiquitous. We generate data from the moment we wake up until the moment we go to sleep, creating trillions of new pieces of information every day. The challenge that comes with such massive amounts of data is managing and storing it efficiently. This is where data centres come into play, helping us retrieve valuable insights and make use of the data we collect.
Let us explore this process in more detail, learning both the role that users and data generators play in this ongoing process. While working with data, you may have encountered terms like databases, data warehouses, data lakes, and data marts.
A Database is a tool where we can store the collection of organized data that is structured. It makes it easily accessible, manageable, updated, and retrievable electronically on a computer system.
Advantages of Databases:
• Minimum data redundancy
• Improved data security
• Increased consistency
• Lower updating errors
• Cost reduction for data entry, data storage, and data retrieval
• Enhanced data access via host and query languages
• Higher application program data integrity
A database management system (DBMS) often has control over a database. A DBMS can upgrade your data processes and increase the business value of your association's data means, freeing users across the organization from repetitious and time-consuming data processing tasks. The result? A more productive pool, better compliance with data regulations, and better opinions.
As an illustration, manufacturing companies create and sell products every day. DBMS is used to maintain records of all these transactions. Just like the railway reservations, In Airline Reservation systems, DBMS is required to keep records of flight arrival, departure, and delay status.
Here is a list of common database management systems:
1. Relational databases
2. Network databases.
3. Object-oriented databases
4. Graph databases
5. ER model databases
6. Document databases
7. NoSQL databases
8. Hierarchical databases
Data warehouses serve as storage for structured and filtered data that has undergone processing for specific purposes. This kind of data is valuable for decision-making as it has been refined for easy dissemination and analysis to a larger audience. Data warehouses also save on expensive storage space by only keeping necessary data, resulting in cost savings for organizations. Furthermore, they facilitate efficient and speedy access to the processed data by organizing it in a structured framework, allowing for faster and more accurate queries.
A Data Lake is a repository of unstructured data with an unclear purpose, while data warehouses store refined and processed data. Compared to data warehouses, data lakes require more storage space and are ideal for quickly analyzing unprocessed data and employing machine learning. However, without sufficient data governance and quality standards, data lakes can become "swamps" of disorganized and unusable data. To address this, an emerging approach combines the management skills of a data warehouse with the flexibility of a data lake.
A data mart is a specialized and curated subset of data that is typically created specifically for use in analytics and business intelligence. These repositories of relevant information are generally designed for a particular subgroup of workers or a specific use case, and they offer a more cost-effective and efficient solution for data storage and analysis due to their smaller and more targeted architecture.
Snowflake's cutting-edge cloud data architecture, which is highly elastic, guarantees that it can accommodate an infinite amount of data and users. Additional compute resources can be spun up quickly to address new use cases without affecting other operations that is happening on the databases thus eliminating the need to spin off separate physical data marts to maintain acceptable performance of the databases.
2.5% to 3.7% of all greenhouse gas emissions come from data centers (source).
The emissions from data centers surpass those from the airline industry (2.4%) and other major economic drivers.
Data storage has a variety of environmental effects, including:
1. GHG Emissions: In 2020, the data centers and networks that support digital technology were responsible for approximately 300 million metric tons of carbon dioxide equivalent emissions, considering not only the energy used during their operation but also the emissions produced during their manufacturing and disposal. This amount is equal to around 0.9% of the total greenhouse gas emissions that come from energy use worldwide or 0.6% of all greenhouse gas emissions. Simply put, the use of digital technology contributes to the emission of greenhouse gases, which have a negative impact on the environment and contribute to climate change.
2. E-Wastes: Data Storage generates a sizable amount of electronic trash (E-trash). Toxic electronic waste exists. In addition to not biodegrading, it also builds up in the ecosystem and degrades the soil and air quality of a region.
3. Battery Backups: In the event of a power outage, data centers employ batteries as a backup. Since they include poisonous, corrosive, and dangerous compounds like lead, lithium, mercury, and cadmium, after they are disposed of, these batteries wind up in landfills and start to have an influence on the environment.
4. Coolant: Coolants are necessary for Computer Room Air Conditioning (CRAC) in Data Centers situated in locations where free cooling or indirect evaporative coolers are prohibited. Although coolants can be used for liquid cooling, chemicals are needed. Chlorofluorocarbons (CFCs), halocarbons, or Freon are frequently used as coolants. These substances range in toxicity from low to high, and prolonged exposure to them can lead to ozone depletion. Since they trap heat in the atmosphere, they also have the potential to contribute to global warming.
5. Cleaning supplies: Dust and filth must be removed for data centers to operate effectively. Utilizing specialized cleaning solutions is the greatest approach to get rid of dust and filth, which are enemies of computer equipment. Since they include bleach, ammonia, and chlorine, most specialized cleaning solutions are harmful. These chemical substances have an impact on human, marine, and natural life. They are linked to ozone loss in the atmosphere, which raises the quantity of ultraviolet (UV) light that reaches the earth's surface.
6. Electronic Waste: Servers require replacing every three to five years due to the limited lifespan of computing equipment. In addition to replacements, there are damaged hard drives, loose bearings, and shattered monitors.
Climate is a particularly contemporary concern for data centers.
According to government estimates, a typical commercial structure uses 10 to 50 times as much energy per square foot as a data center. The shaky figures on water use that are not always published further confounded these calculations.
A data center that makes use of energy-efficient technology is considered carbon-neutral. neutral for carbon Data centers play a key role in the IT industry's quest towards sustainability.
• Examining data center architecture
• The system management feature
• Asset locating
• Energy Administration
• Capacity Arrangement
The Formula to Calculate WUE: WUE = Total Water Used by the Facility/ Energy consumed Solely by the IT Equipment
Higher the WUE, more water Intensive the data Center is
The Formula to Calculate CUE
CUE = CO2 Emissions Caused by Total Data Center Energy/ IT Equipment Energy
Ideal PUE Value is 1.0 Indicates that all energy consumed by Data Center is used to power actual computing devices.
The best data Center in the World achieved a PUE = 1.2
Businesses who want to make better use of their data need, simply: reliable and sustainable storage solutions – a cornerstone for organizing, processing and communicating their information. We’ve worked with Fortune 500 companies and SMEs alike in streamlining, optimising and securing storage assets for our clients. Get in touch to learn how sustainability and data storage can work hand in hand.