By Nikhil Sonawane - June 14, 2023 6 Mins Read
Modern enterprises gather data from various sources faster than before, regardless of size, industry, or type. However, data can yield better results only when used wisely and in real time.
Chief Data Officers must decide whether to select or develop a data warehouse or data lake for their organization. Businesses today need a repository that stores all the streaming data quickly and easily. Data warehouses will not help businesses to achieve their goals.
If resources cannot question, model, and evaluate that data when it is fresh to get valuable business insights, all the efforts to gather and process data will be in vain. Hence, this limitation makes data lakes a less preferred choice of the IT teams.
Cloud architects always consider embracing warehouse restrictions and data lake inefficiencies. A few businesses have adopted a newer hybrid concept, the data lake house, to meet their organization’s growing data needs.
However, before decision-makers embark on a journey to buy or build a data warehouse or a data lakehouse, they must be aware of potential limitations and benefits to make the right decisions.
The ways IT teams can overcome the confusion of selecting the right data repository for their organization are:
Embracing a data warehouse in the tech stack will offer a centralized data repository to save large amounts of data sets gathered from various sources within the enterprise. This data repository is a single source of truth in a business and is used for core reporting and business analytics.
Business decision-makers can use a data warehouse to save historical data by collating relevant data sets from various sources such as application, business, and transactional data. A data warehouse is an effective tool that helps organizations to extract data from various sources and transform and clean it before ingesting it into a repository. Most data leaders allocate their budgets to buy or develop a data warehouse because of its capability to offer valuable business insights throughout the organization quickly.
Business analysts, decision-makers, and data engineers can use a data warehouse to access data through business intelligence (BI) tools, SQL clients, and other analytics applications. Enhancing data standardization, quality, and consistency is one of the most significant benefits businesses can get after adopting a data warehouse.
Modern businesses gather data from multiple sources, such as sales, users, and transactional data. Adopting a data warehousing approach consolidates all the corporate information into a uniform, standardized format which acts as a single source of data truth, offering the organization the confidence to depend on the data for all their business needs.
Businesses can benefit from embracing a data warehouse by optimizing business intelligence and improving the accuracy and speed of data analytics. It is one of the most effective ways to enhance the overall decision-making process of their organization. Even though data warehouse offers a significant advantage for businesses, there are a few limitations, like the lack of data flexibility it exposes to businesses.
Data warehouses perform better with structured data, but they can find it to work with semi-structured or unstructured data. High implementation and maintenance costs also hinder the adoption of data warehouses in many organizations.
Adopting a data lake will offer enterprises with unified, flexible data storage repository that saves huge amounts of structured and unstructured information in its raw, authentic, and unformatted form. Data lakes store data in contrast to data warehouses. Warehouses store cleaned and relevant data, whereas data lakes save information utilizing a flat architecture and object storage in its raw form.
Embracing a data lake approach will offer businesses a flexible, durable, and cost-efficient solution to get valuable insights from unstructured data, unlike data warehouses that find it challenging to process data in this format. Data lakes do not have a well-defined schema when the information is gathered. Rather data lakes extract, load, and transform (ELT) for evaluation purposes. Organizations that gather data from IoT devices, social media, and streaming data can use data lakes to allow machine learning and predictive analytics of the data gathered through all the sources.
Data consolidation, flexibility, and cost savings are significant benefits businesses can get from embracing a data lake. These data repositories will support many data science and machine learning use cases. Similar to data warehouses, data lakes have some inherent limitations that it imposes on businesses.
Businesses that embrace a data lake will lack data reliability and security. The inconsistencies of a data lake make it difficult for organizations to ensure data reliability and security. Organizations that do not manage data lakes effectively can become extremely disorganized, making connecting with the BI and analytics tools difficult.
Implementing a data lakehouse offers businesses new, big-data storage architecture with the best functionalities of data warehouses and data lakes. This data repository is a single source for all the structured, semi-structured, or unstructured data.
Moreover, it also has machine learning (ML), business intelligence (BI), and streaming capabilities to optimize data management. Reduced data redundancy and cost-effectiveness make data lakehouse a viable option for businesses. Streamlined data versioning, governance, security, and assistance in various workloads are significant benefits of data lakehouse.
Data Lakehouse is still a new technology. Competing with other robust big-data storage solutions might take a long time.
Even though a data warehouse is a legacy big-data storage technology with a proven success history in business intelligence, reporting, and analytics applications, they are expensive and make it challenging to process unstructured data like streaming and data with variety.
Businesses started embracing data lakes to manage raw data in various formats on affordable storage for machine learning and data science tasks. Although data lakes work effectively with unstructured data, they will not have transactional functionalities like the data warehouses making it challenging to ensure data consistency and reliability.
Data Lakehouse is the latest data storage framework that offers the cost-efficiency and flexibility benefits like the data lakes with the reliability and consistency that data warehouse can offer.
The data lakehouse, data warehouse, or data lake conundrum will continue to evolve. Hence decision-makers need to be aware of their needs before making final decisions about data storage. If businesses do not have an in-house expert, they can consult a third-party expert to make the right decisions to gather, store and process the data without compromising security.
Nikhil Sonawane is a Tech Journalist with OnDot Media. He has 4+ years of technical expertise in drafting content strategies for Blockchain, Supply Chain Management, Artificial Intelligence, and IoT. His Commitment to ongoing learning and improvement helps him to deliver thought-provoking insights and analysis on complex technologies and tools that are revolutionizing modern enterprises. He brings his eye for editorial detail and keen sense of language skills to every article he writes. If he is not working, he will be found on treks, walking in forests, or swimming in the ocean.
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