In the fast-paced world of technological advancements, companies are constantly searching for new ways to capture data from various sources and use it strategically. A data warehouse, data lake, or a data lakehouse can all be utilized in order to meet these needs — but which is best for your organization? It all depends on your specific requirements.
Data warehouses are designed for more traditional models and cannot efficiently store streaming data; meanwhile, a data lake may not provide quite enough query models or fresh enough data to complete all tasks you require. As a result, newer concepts such as the "data lakehouse" have been developed in order to address these needs. To choose the right option for your organization's goals, become familiar with each option and decide what is most suitable. Let's explore further!
What Is Data Warehouse?
Data warehouses provide useful insights for organizations by storing structured business data in a central location. Before writing to the warehouse, the schema must be known. Data is regularly pulled from different sources and then formatted to match the existing schemas in the warehouse. Designed for business analysts and less tech-savvy users, data warehouses are well-suited for applications such as BI dashboard, batch reports, and visualizations.
Data warehouses stand apart from other databases due to their optimized storage and processing capabilities, allowing them to answer queries quickly. Moreover, cloud solutions for data warehouses are available through Amazon Redshift, Google Big Query, Snowflake, and Azure SQL Data Warehouse – offering businesses high-quality data with fast query results at their fingertips. Data warehouses do not support raw or unstructured data, and managing the ever-increasing data is expensive. Further, processing complex data types like machine learning and predictive analytics could be better accomplished using alternative methods.
Data Warehouse Architecture
Data warehouses are structured data repositories composed of four core components.
- Central database: The main database is the basis of any data warehouse, storing objects organized into tables.
- Data integration tools: Data integration tools connect disparate sources and format the raw digital information to be stored properly in the data warehouse. These tools are typically implemented through an Extract More Transform Less (EMTL) strategy or Extract Load Transform (ELT) method.
- Metadata: Metadata contextualizes organizational data about other data points, giving us more information about them than just their content alone.
- Data access tools: Users need to be able to interact with the stored digital info in a data warehouse; this is accomplished by software such as query, application development, data mining, and OLAP tools.
Advantages Of Data Warehouse
Data warehouses are highly-organized data repositories intended for easy access to ready-to-use data. There are several benefits of data warehouse, including:
- Maintains data consistency and standardization: A warehouse provides a single, comprehensive source of data truth by unifying and standardizing the varying data sources across all the applications and tools used within an organization. As a result, data consistency is ensured and multiple sources of data can be easily uploaded.
- Effortless data access: Data accessibility is key for teams to work efficiently. As such, data transformation has already been done, meaning that all the work is ready for use with minimal preparation required. This allows teams to use the data immediately and maximize their productivity.
Disadvantages Of Data Warehouse
Despite of being several advantages, it also has some disadvantages:
- Data May Lost & Costly Process: When dealing with large datasets, some data may be discarded due to an inability to fit into the warehouse organization structure. This can prove costly and inefficient in the frequent data processing.
Data Warehouse Use Cases
Organizations use data warehouses to gain rapid insights into their large-scale datasets. Data warehouses provide high-velocity analytics on petabytes of stored historical information. Users can query these solutions and generate BI-type reports analyzing, for instance, sales during a certain period, broken up by region or division, and any changes between years.
Data warehouses are used in many different applications, such as transactional reporting, ad-hoc analysis and reporting, data mining, dynamic presentation through data visualization, and drilling down to the details of hierarchical dimensions.
- Transactional reporting is relied upon to present an accurate picture of business operations, while ad-hoc analysis is employed to respond singularly or "one-off" challenges.
- Data mining processes uncover hidden patterns in data sets that can be complicated. Data visualization allows users to access real-time metrics swiftly while drilling down assists with discovering more detailed information under hierarchical levels of a given topic.
Structured business data is critical in any organization with mature data practices. However, traditional data warehouses may not be able to support modern big data technologies and may become outdated due to periodic batch updates. That's why it is important to have an efficient way of accessing and aggregating such data outside operational databases.
Overall, data warehouses are a great way to store and access structured data. But with the average person producing 2.5 quintillion data bytes daily, most of which is unstructured, organizations need to turn to other solutions for long-term storage. Fortunately, there's an answer – a data lake. Data lakes allow companies to store raw data to access it later when needed easily.
What Is Data Lake?
Data lakes are efficient and scalable centralized repositories used for data storage, often designed to store significant amounts of structured and unstructured raw data. Incoming data can be quickly dumped into the lake and stored in its original format. Data engineers then use this tracked source of information to run analytics or generate reports, providing better insights and decision-making capabilities across your organization.
The challenge with a data lake is that it requires proper curation to remain organized; otherwise, it can become difficult to locate the desired piece of data when needed. Despite this, a cost-effective and flexible solution makes data lakes invaluable to organizations seeking reliable storage solutions.
Data Lake Architecture
The ISASA data implementation pattern, which includes five steps - Ingest, Store, Analyze, Surface, and Act - is essential for a successful data lake.
- In the Ingest phase, teams utilize ETL tools such as Renta's Marketing ETL to transfer data through APIs or batch processes quickly.
- Then comes the Storage: structured and unstructured data are gathered in a centralized repository, with Amazon S3, Google Cloud Storage, and Azure Data Lake Storage Gen2 being popular choices.
- Next is Analyze: here it conducted a deep analysis of the collected information by uncovering any potential links.
- Once that's done, we move to the Surface when the business intelligence (BI) tools are employed to visualize results in graphs, charts, and diagrams.
- And finally, the team adjusts their management decisions concerning marketing strategies according to what has been uncovered.
Advantages Of Data Lake
Data lakes provide organizations with a safe, reliable option for storing extensive amounts of data across numerous mediums and in any format. Check out the benefits of data lake here.
- This structure offers many advantages, such as data flexibility, which ensures no information is lost during transformation and more opportunities for analytics that utilize the different machine and deep learning algorithms.
- In addition, data lakes are stored on low-cost commodity hardware –– thus making it a cost-effective solution compared to traditional data warehouses.
Disadvantages Of Data Lake
- Careful supervision is essential. Due to the unstructured nature of data lakes, high-quality data can be easily overshadowed by superfluous or low-quality material. Without strong management, data lakes are likely to become overrun and unwieldy.
- Reliability and security must be considered. With warehouses, data protection is more straightforward than with a data lake; sensitive information cannot always be kept secure in a lake environment. For this reason, proper policies governing reliability and security must be established before the lake's implementation begins.
Data Lake Use Cases
Data lakes provide businesses instant access to all their data from multiple sources. Data lakes let you look at yesterday's events, inform current happenings (stream analytics), and forecast future outcomes (predictive analytics, machine learning).
Data lakes have numerous benefits for businesses as they offer a wide range of uses:
- feeding enterprise data warehouses with datasets;
- executing stream analytics tasks; employing ML projects; building complex analytics graphics using enterprise BI tools like Tableau or MS Power BI;
- Crafting custom data analytics solutions and running root cause analysis that enables teams to find the origin of issues.
Thanks to their effective data engineering capabilities, organizations can unlock hidden business opportunities with the help of data lakes. Data lakes are powerful tools that allow teams to experiment with different resources and understand how they can be utilized more effectively. This includes generating models that sift through existing data and test new schemas to view it from various perspectives. Other uses include handling streaming data from web logs or IoT sensors which isn't feasible with standard databases. To summarize, data lakes create avenues for organizations to find patterns, predict future trends, or discover business possibilities by understanding new products or streamlining current structures.
Data lakes are cost-effective, inexpensive object storage solutions that can provide organizations with scalability. However, if left unmanaged, they can easily become cluttered data swamps where finding or working with any user data is difficult. Companies must maintain organization and structure within the data lake to avoid this.
What Is Data Lakehouse?
A data lake house is a recently developed hybrid solution that takes the most beneficial elements from both a data warehouse and a data lake. This novel technology allows organizations to store structured and unstructured data in one repository, resulting in more efficient data governance, scalability, and cost savings. Moreover, this method relieves the limitations of traditional formats, such as a lack of support for advanced analytics in warehouses and the complexity of lakes. In sum, leveraging a data lake house gives companies access to real-time and historical insights with greater ease.
What Is Lakehouse Architecture?
- The data lakehouse architecture comprises five integral layers: Ingestion, Storage, Metadata, Processing, and Governance. The Ingestion Layer allows data to be obtained from multiple sources and delivered to the database.
- The Storage Layer is a cloud-based object which accommodates various forms of data, including structured and unstructured data; it essentially serves as the backbone of the Data Lake architecture.
- The Metadata Layer provides a comprehensive catalog containing metadata surrounding objects in storage; this layer enables easy indexing, ACID transactions (Atomicity, Consistency, Isolation, Durability), schema enforcement, and other management protocols characteristic of the organization.
- The API Layer enables access to data assets by allowing users to rapidly pull the data from the storage and process their tasks. It assists in working out what data is required for a specific application and how it should be retrieved. In addition, certain machine learning libraries, for example, TensorFlow and Spark, that utilize open file formats like Parquet can query metadata directly. Furthermore, developers can form structures on and transform data from multiple sources with DataFrame APIs.
- The Consumption Layer comprises business tools, apps, and workflows using the extracted data for analytics reporting, BI, and AI tasks.
Advantages Of Data Lakehouse
Data lake houses have advantages from both data warehouses and data lakes: they combine useful features of both solutions. Check out the benefits of data lakehouse.
- These hybrid systems allow for various workloads, including advanced analytics and machine learning.
- Furthermore, their computing layer gives organizations an intelligent means to manage the data and avoid becoming swamped.
- It is also cost-effective; utilizing object-oriented files such as JSON or Parquet (optimized for lakehouse) minimizes expenses while obtaining optimal results.
- Last, the architecture facilitates easy implementation of security and governance mechanisms.
Data Lake House Use Cases
Data lake houses provide several potential use cases. These include:
- The analysis of clickstream data, with storage for some collected data for reporting purposes and other parts used for more in-depth analytics.
- Additionally, larger datasets may be created by integrating multiple warehouse sources to improve product recommendations. Finally, purpose-built stores may move their data into the lake house to benefit from its data gravity benefits.
Disadvantages Of Data Lakehouse
A lakehouse pattern is a novel approach; however, it is yet to be proven how effective it can be. Therefore, data lake houses may have a different widespread use than more established system designs for some time. Nevertheless, this technology promises to deliver promising results, and it will be interesting to see what further improvements are made in the future.
Data Warehouse Vs Data Lake Vs Data Lakehouse
Here is the quick comparison of data warehouse vs data lake vs data lakehouse
Data Warehouse Vs. Data Lake Vs. Data Lakehouse: Best Practices To Choose
When exploring data storage options, it's essential to ask WHY. Why do you need the solution, and for what purpose? Are you addressing everyday reporting needs, business intelligence and real-time analytics, data science, or general analytical conditions? Also, determine which is more important for your business: data consistency or timeliness. This helps narrow down the choices between a data warehouse, lakehouse, or lake.
It's critical to recognize that key differences between these systems lie in something other than the technology but in how they meet various business requirements. Additionally, even with stakeholders' buy-in, it can still be a challenging project. Understanding your specific needs is integral to finding the right answer as offerings and pricing models from vendors continue to evolve rapidly.
To ensure that your analytics needs are identified and fulfilled, spending time developing use cases is very important. This should be done while understanding your users and the skillsets required to perform different tasks.
The following are general guidelines to follow:
- A data warehouse is a way to go if you have precise questions while understanding what analytics results you expect regularly.
- A data warehouse is essential for compliance if your industry, such as healthcare or insurance, has strict reporting regulations.
- For historical analysis regarding KPIs or performance metrics, a data lake or hybrid solution would be unnecessary; instead, opt for a data warehouse.
- Experimental and exploratory analysis may require unstructured data from a data lake or hybrid solution; however, advanced analytics skills may also need to be employed to get your desired results.
- Data mature organizations that want to leverage machine learning technology should choose a hybrid solution or data lake, depending on their requirements.
When planning a data storage system, it is important to consider budget and time restrictions. While data lakes are generally easier and inexpensive to build, data warehouses can offer higher performance. As your organization grows, so should the capabilities of your storage system; incremental updates can help with this.
If you're considering replacing an existing legacy system, make sure you have valid reasons. Does it run queries too slowly or miss certain bits of crucial data? You'll need a concrete business case that shows clear returns on investment before making any decisions about retiring the old model.
Remember that the technologies and architectures for data storage continue to evolve rapidly. It may not be possible to predict how they will change in the future, but knowledge of common mistakes may prove helpful when navigating current models.
Data lakehouses are the perfect solution for businesses seeking to leverage automation with the combined power of data lakes and data warehouses. Data lakes store information in a raw format, while data warehouses store structured and pre-processed data optimized for analysis. A data lakehouse provides a single repository for both types of data, allowing businesses to capitalize on the advantages of both systems. If you still need clarification, which to choose? Look no further than Agilisium to guide you through the best processes and practices regarding your organization's data storage strategy.