What is a Data Lake?
One of the most popular storage repositories is the data lake. Mainly, it stores data regardless if it is unstructured, semi-structured, or structured.
If you are still unsure on how to deal with your data or you feel that it is not yet enough for an accurate analysis, you can keep it here.
You can store a wide range of data in its native format without limiting the size of the file or the account. It has excellent data volume for enhanced analysis including native integration.
This type of storage repository is used to store massive amounts of raw data in its original format until you need it. If you are a type of company that stores a huge amount of data before getting a data scientist to look into it, then this is a great option for you.
Data lakes offer more flexibility than data warehouses yet it could be overwhelming if you look into it without a planned analysis.
As data lakes are mainly used for data science research and testing, they mainly use data scientists and engineers.
A data lake is where the data is dumped and temporarily stored. A company building a data warehouse that essentially keeps all the data on-hand until the warehouse is operational.
For small and medium-sized companies, a data lake is likely to be of little or no use.
An Overview of Data Lake Concepts
Raw data are preserved in a Data Lake in their original format until the time they are needed. Identifiers and expanded metadata tags are assigned to each data element in a Data Lake. It has a variety of analytic capabilities.
What is a Data Warehouse?
Data warehouses are large storage areas for a variety of data gathered from different sources. Businesses have used data warehouses for years to store and gather business intelligence and data. Data analyses involving these structures are limited in what they can cover.
A data warehouse combines technology and components so you can strategically manage and use your data. Data is collected and managed from multiple sources. A business needs to analyze the data to provide useful insights to business owners.
This method of storing large amounts of data is used for queries and analysis rather than transaction processing. Data is transformed into information in this process.
An essential advantage of having a data warehouse for mid and large-sized businesses is that they can share content across department-specific databases.
Organizations become more efficient by utilizing data warehouses. A data warehouse can help companies make more informed, data-driven decisions.
An Overview of Data Warehouse Concepts
Data Warehouses store data in folders. When they are kept in files, it will be easier for you to make strategic decisions. Additionally, it presents atomic and summary data in a multi-dimensional manner. There are several crucial tasks to perform, namely:
- Data Cleaning
- Data Loading and Refreshing
- Data Extraction
- Data Transformation
Data Lake and Data Warehouse: Which Should You Choose?
With a data lake, the data is stored regardless of its source and structure. The data are being stored in their raw form. You only transform it when you are ready to use it.
The data warehouse can only store structured data.
A data warehouse consists of data that is pulled from transactional systems. The data warehouse then summarizes the data into metrics. The data stored is processed, transformed, and cleaned.
In a data lake, the data is in its raw form. To make use of data, you need to give it shape and structure. This kind of processing is known as schema-on-read.
With data lakes, users can access data before it is transformed, cleaned, and structured. Users can get to their results more quickly than in a traditional data warehouse.
Data must first have some form and structure before it can be loaded into a data warehouse. This is called a model. Providing data with structure and form is known as schema-on-write.
The data warehouse enables insights into predefined questions for predefined data types. So any changes to the data warehouse will take more time.
Using data lake storage technologies is usually less expensive compared to using a data warehouse.
Since open-source technologies are often used in data lakes, licensing and community support are often free. Data lakes are designed to run on commodity hardware at a low cost.
Keeping data in a Data warehouse is expensive. Others find it time-consuming.
The cost of storing a data warehouse may be high, especially if a large volume of data needs to be stored.
Data lakes lack structure. It allows data scientists and developers to easily configure and modify data models, queries, and applications. Therefore, data scientists and analysts, not employees, work in data lakes mainly for testing and research. The lack of structure makes non-experts feel uneasy about operating data lakes.
Data warehouses are highly structured data storage with a fixed configuration and limited agility. Technically, changing the structure isn’t too difficult, but the process takes a long time when you consider all the business processes that rely on the warehouse.
Stored data can range from customer information to business expenses. It can be too overwhelming with the regular influx of data. This is why data management softwares like SAP database and applications are useful to get more insights.
If you need SAP services to handle your database, NTT Data Philippines can help you manage and discover various SAP solutions like SAP S/4Hana.
Technology based on big data, containing data lakes, is relatively new. The data lake has limited capacity to secure data because of this. Luckily, a lot is being done to protect data security, and it’s maturing rapidly.
Technology for developing data warehouses has been around for decades. Data warehouses are significantly more secure and mature than data lakes.
Data lakes are ideal for data scientists who like to do in-depth analysis. They require advanced analytics tools with statistical modeling and prediction capabilities.
This well-structured data storage is easy to use and understand. Your operations team can easily access and interpret the information.
Data Lakes use the Extract Load Transform process.
Data warehouses use the Extract Transform Load which is the traditional way to process data.
Analysts use data warehouses because they may require going beyond their capabilities. They have different data types with no insights yet. They need to set rules and algorithms to analyze the data and see how this information can help the company’s growth.
The majority of users are in the operations team. They use a data warehouse to see performance metrics and reports.
Gone are the days when you can keep all your data in folders. If you are a growing business in a highly competitive industry, your data is the best asset you’ll have. You can’t leverage your data if you do not know where to keep it safe, secured and organized. While Data Lake and Data Warehouse have generally the same purpose, they give massive benefits based on what stage your business is in. Take the time to balance your pros and cons to manage them successfully.