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Companies are capturing more data than ever as they increasingly rely on it to inform important business decisions, enhance product offerings, and provide better customer service.
With the quantity of data being created at an exponential rate, the cloud offers several advantages for data processing and analytics, including scalability, dependability, and availability.
In the cloud ecosystem, there are also several tools and technologies for data processing and analytics. The two types of big data storage structures that are most frequently utilized are data warehouses and data lakes.
Although utilizing a data lake is less appealing since you can’t query the model and data while it is still relevant, employing a data warehouse for streaming data storage is wasteful.
Which type of cloud architecture do we choose?
Should we consider newer concepts for the data lakehouse, or should we be content with the warehouse’s constraints or the lake’s restrictions?
A novel data storage architecture called a “data lakehouse” combines the adaptability of data lakes with the data management of data warehouses.
Understanding the various big-data storage methods is essential for building a reliable data storage pipeline for business intelligence (BI), data analytics, and machine learning (ML) workloads, depending on your company’s demands.
In this post, we will closely look at Data Warehouse, Data Lake, and Data Lakehouse, with benefits, limitations as well as pros and cons of them. Let’s begin.
What is Data Warehouse?
A data warehouse is a centralized data repository used by an organization to hold enormous volumes of data from many sources. A data warehouse acts as an organization’s single source of “data truth” and is essential to reporting and business analytics.
Typically, data warehouses combine relational data sets from several sources, such as application, business, and transactional data, to store historical data. Before being loaded into the warehousing system, data is transformed and cleaned in data warehouses so that it can be used as a single source of data truth.
Due to their capacity to swiftly offer business insights from all areas of the company, businesses invest in data warehouses. With the use of BI tools, SQL clients, and other less sophisticated (i.e., non-data science) analytics solutions, business analysts, data engineers, and decision-makers can access data from data warehouses.
It is expensive to maintain a warehouse with the ever-increasing volume of data, and a data warehouse cannot handle raw or unstructured data. Additionally, it is not the ideal option for sophisticated data analysis techniques like machine learning or predictive modeling.
A data warehouse, therefore, provides faster query responses and data of a higher quality. Google Big Query, Amazon Redshift, Azure SQL Data warehouse, and Snowflake are cloud services that are available for data warehouses.
Benefits of Data Warehouse
- Increasing the efficiency and speed of business intelligence and data analytics workloads: Data warehouses shorten the time needed for data preparation and analysis. They can easily link to data analytics and business intelligence tools since the data from the data warehouse is reliable and consistent. Additionally, data warehouses save the time needed for data collection and provide teams the ability to use data for reports, dashboards, and other analytics requirements.
- Increasing the consistency, quality, and standardization of data: Organizations collect data from a variety of sources, including user, sales, and transactional data. The firm can trust the data for business requirements because data warehousing compiles corporate data into a uniform, standardized format that can act as a single source of data truth.
- Enhancing decision-making in general: Data warehousing facilitates better decision-making by offering a centralized store for both recent and old data. By processing data in data warehouses for precise insights, decision-makers can assess risks, comprehend client wants, and enhance goods and services.
- Providing better business intelligence: Data warehousing bridges the gap between massive raw data, which is frequently collected routinely as a matter of course, and the curated data that provides insights. They act as the foundation for an organization’s data storage, enabling it to answer complicated questions about its data and utilize the responses to make defensible business decisions.
Limitations of Data Warehouse
- Lack of data flexibility: While data warehouses excel at handling structured data, semi-structured and unstructured data formats like log analytics, streaming, and social media data can be challenging for them. This makes recommending data warehouses for use cases involving machine learning and artificial intelligence difficult.
- Costly to install and maintain: Data warehouses can be expensive to install and maintain. Furthermore, the data warehouse is often not static; it ages and needs frequent upkeep, which is expensive.
- Data is simple to find, retrieve, and query.
- As long as the data is already clean, SQL data preparation is simple.
- You are forced to use only one analytics vendor.
- Analyzing and storing unstructured or flowing data is quite costly.
What is Data Lake?
Every type of data is promised and made possible by data lakes. It is beneficial to have data in an accessible manner centrally located and available for reading.
A data lake is a centralized, extremely adaptable storage space where massive volumes of organized and unstructured data are kept in their unprocessed, unaltered, and unformatted forms.
A data lake employs a flat architecture and objects stored in its unprocessed state to store data, as opposed to data warehouses, which save relational data that has previously been “cleaned.”
Data lakes, as opposed to data warehouses, which have difficulty handling data in this format, are adaptable, reliable, and affordable and allow enterprises to obtain enhanced insight from unstructured data.
In data lakes, data is extracted, loaded, and transformed (ELT) for analytical purposes rather than having the schema or data established at the time of data gathering.
Utilizing technologies for many data kinds from IoT devices, social media, and streaming data, data lakes enable machine learning and predictive analytics.
Additionally, a data scientist who can process raw data can use the data lake. A data warehouse, on the other hand, is easier for businesses to use. It is perfect for user profiling, predictive analytics, machine learning, and other tasks.
Although data lakes address several issues with data warehouses, their data quality is poor and their query speed is insufficient. Additionally, it takes extra tools for business users to conduct SQL queries. A data lake that is poorly structured may experience an issue with data stagnation.
Benefits of Data Lake
- Support for a wide range of machine learning and data science application cases It is simpler to use a different machine and deep learning algorithms to handle the data in data lakes since the data is kept in an open, raw manner.
- Data lakes’ versatility, which allows you to store data in any format or media without the requirement for a preset schema, is a big advantage. Future data use cases can be supported, and more data can be analyzed if the data is left in its original state.
- In order to avoid having to store both types of data in various contexts, data lakes can contain both structured and unstructured data. For the storage of various kinds of organizational data, they offer a single location.
- Compared to traditional data warehouses, data lakes are less expensive because they are built to be kept on inexpensive commodity hardware, such as object storage, which is often geared for a lower cost per gigabyte stored.
Limitations of Data Lake
- Data analytics and business intelligence use cases score poorly: Data lakes can become unorganized if they are not adequately maintained, which makes it difficult to link them to business intelligence and analytics tools. Additionally, when necessary for reporting and analytics use cases, a lack of consistent data structures and ACID (atomicity, consistency, isolation, and durability) transactional support can lead to suboptimal query performance.
- Data lakes’ inconsistency makes it impossible to enforce data dependability and security, which results in a lack of both. It may be difficult to develop appropriate data security and governance standards to cater to sensitive data types, since data lakes can handle any data form.
- Solutions that are affordable for all types of data.
- Able to handle data that is both organized and semi-structured.
- Ideal for complicated data processing and streaming.
- Needs a sophisticated pipeline to be built.
- Give data some time to become queryable.
- Takes time to guarantee data dependability and quality.
What is Data Lakehouse?
A novel big-data storage architecture called a “data lakehouse” combines the greatest aspects of data lakes and data warehouses. All of your data, whether structured, semi-structured, or unstructured, can be stored in one location with the finest machine learning, business intelligence, and streaming capabilities possible thanks to a data lakehouse.
Data lakes of all sorts are often the starting point for data lakehouses; after that, the data is transformed into Delta Lake format (an open-source storage layer that brings reliability to data lakes).
Data lakes with delta lakes enable ACID transactional procedures from conventional data warehouses. In essence, the lakehouse system uses inexpensive storage to maintain massive amounts of data in their original forms, much like data lakes.
Adding the metadata layer on top of the store also gives data structure and empowers data management tools like those found in data warehouses.
This makes it possible for many teams to access all of the company data through a single system for a variety of initiatives, such as data science, machine learning, and business intelligence.
Benefits of Data Lakehouse
- Support for a larger range of workloads: To facilitate sophisticated analyses, data lakehouses give users direct access to some of the most popular business intelligence tools (Tableau, PowerBI). Additionally, data scientists and machine learning engineers can easily use the data since data lakehouses employ open-data formats (such as Parquet) together with APIs and machine learning frameworks, such as Python/R.
- Cost-effectiveness: Data lakehouses employ inexpensive object storage solutions to implement data lakes’ cost-effective storage characteristics. By offering a single solution, data lakehouses also do away with the expenses and time associated with managing various data storage systems.
- Data lakehouse design ensures schema and data integrity, making it simpler to build effective data security and governance systems. Ease of data versioning, governance, and security.
- Data lakehouses offer a single, multipurpose data storage platform that can accommodate all company data demands, which reduces data duplication. The majority of businesses choose a hybrid solution due to the benefits of both the data warehouse and the data lake. This strategy, meanwhile, could result in costly data duplication.
- The support of open formats. Open formats are file types that can be used by many software applications and whose specifications are publicly available. According to reports, Lakehouses are capable of storing data in common file formats like Apache Parquet and ORC (Optimized Row Columnar).
Limitations of Data Lakehouse
A data lakehouse’s biggest drawback is that it is still a young and developing technology. It’s uncertain if it will fulfill its commitments as a result. Before data lakehouses can compete with established big-data storage systems, it could take years.
However, given the rate at which modern innovation is occurring, it is difficult to say if a different data storage system won’t ultimately replace it.
- One platform has all of the data, which means there are fewer hostnames to maintain.
- Atomicity, consistency, isolation, and toughness are unaffected.
- It is significantly more affordable.
- One platform has all of the data, which means there are fewer hostnames to maintain.
- Simple to manage, and quick to remedy any issues
- Make it simpler to construct a pipeline
- Setting up may take some time.
- It is too young and too far away to qualify as an established storage system.
Data Warehouse Vs Data Lake Vs Data Lakehouse
The data warehouse has a long history in corporate intelligence, reporting, and analytics applications and is the first big-data storage technology.
Data warehouses, on the other hand, are pricey and have trouble handling diverse and unstructured data, such as streaming data. For machine learning and data science workloads, data lakes were developed to manage raw data in diverse forms on affordable storage.
Although data lakes are effective with unstructured data, they lack the ACID transactional capabilities of data warehouses, making it challenging to guarantee data consistency and dependability.
The newest data storage architecture, known as the “data lakehouse,” combines the dependability and consistency of data warehouses with the affordability and adaptability of data lakes.
In conclusion, building a data lakehouse from scratch might be difficult. Furthermore, you’ll almost certainly be using a platform designed to enable open data lakehouse architecture.
Therefore, make cautious to investigate the many features and implementations of each platform before making a purchase. Companies looking for a mature, structured data solution with a focus on business intelligence and data analytics use cases can consider a data warehouse.
However, enterprises looking for a scalable, affordable big data solution to power workloads for data science and machine learning on unstructured data should consider data lakes.
Consider that your business needs more data than the data warehouse and data lake technologies can provide, or that you’re seeking for a solution to integrate sophisticated analytics and machine learning operations on your data. A data lakehouse is a sensible option in the situation.