With a data lake, different types of data can be stored and processed regardless of size.
The use of the data lake is suitable for a wide range of industries, e.g. retail, banking or hospitality. The goal is to predict customer preferences and improve the customer experience.
Everything you need to know about the data lake and its use in your business can be found here.
This article was written in German, automatically translated into other languages and editorially reviewed. We welcome feedback at the end of the article.
Data Lake: Definition
A data lake is a low-cost storage environment that houses petabytes of raw data. Unlike a data warehouse, a data lake can store both structured and unstructured data and does not require a defined schema to store data.
This feature, known as "schema-on-read," allows for great flexibility in storage requirements and is especially useful for Data Scientists, Data Engineers, and Developers who need to access data for data discovery exercises and machine learning projects.
Attention: data swamp and data pits
Although data lakes are becoming increasingly popular with users, there is a risk of getting stuck in data quagmires or data pits.
A Data Swamp arises from poor management of the data lake, where there is a lack of proper data quality and data governance practices to extract valuable insights. Without proper monitoring, the data in these repositories becomes useless.
Data Pits resemble data quagmires in that they offer little business value, but the cause of the data problem in these cases is unclear.
To avoid these dangers, it is important to involve data governance and data science teams.
Data Lake: Cloud or on-premise?
The cloud can be the optimal choice for some businesses to store their data. This is because of the additional benefits it offers - flexible scalability, rapid service delivery and efficient IT solutions - as well as a subscription-based billing model.
Cloud Data Lake
A data lake is a centralized storage location that holds all critical enterprise data and serves as an easily accessible staging area.
This enables access to all business data, including that used by on-premise applications and cloud-based applications that can handle Big Data.
The decision of whether to locate a data lake in the cloud or on-premise depends on a number of factors and must be carefully considered.
While a cloud-based data lake offers the benefits of scalability and flexibility, an on-premise data lake can provide greater control and security.
Ultimately, the choice of location depends on the specific requirements of the business.
Data Lake on site
Businesses often have similar reasons for keeping their data lake in-house as they do for managing a private cloud on-premises.
This approach provides the highest level of security and control, which can protect intellectual property and business-critical applications. In addition, sensitive data can be retained in compliance with regulatory requirements.
However, there are also disadvantages to managing a data lake in-house, which can also occur when managing a private cloud on-premises. Both can lead to increased internal maintenance of the data lake architecture, hardware infrastructure and associated software and services.
Hybrid Data Lake
Enterprises can opt for a hybrid data lake, where the data lake is split between on-premises and the cloud.
In such architectures, business-critical data is not normally stored in the cloud data lake. If personally identifiable information (PII) or other sensitive data is nevertheless included, it is obscured or anonymized to ensure compliance with data security and privacy policies.
To minimize cloud storage costs, data stored in the cloud can be deleted on a regular basis or after pilot projects are completed. This is an effective way to ensure data security while keeping costs in check.
Data Lake vs. Data Warehouse
Both data lakes and data warehouses are used for data storage, but both repositories have different storage requirements, making them ideal for different scenarios.
Data warehouses need, for example, a defined scheme, to meet specific data analytics requirements set by business users and other relevant stakeholders.
These requirements are essential for regular report use and the underlying system is typically relational and structured. It pulls data from transactional databases and is ideal for business intelligence tasks such as dashboards and data visualizations.
In contrast, integrate Data Lakes Data from relational and non-relational systems, enabling Data Scientists to Structured and unstructured data be able to integrate into more data science projects.
Each system has its own strengths and weaknesses.
An example of this is the fact that data warehouses are generally more powerful, but also have higher costs. In contrast, data lakes may be slower at returning query results, but offer lower storage costs. In addition, the storage capacity of data lakes is optimal for business data.
Data Lake vs. Data Lakehouse
A Data Lake is a centralized repository that stores raw, unstructured, semi-structured and structured data of any size.
It provides a way to store data in its native format without the need for predefined schemas or data transformations, making it more flexible and agile compared to traditional data storage solutions.
However, data stored in a data lake can lack quality and consistency, which can cause problems when trying to derive insights from the data.
A Data Lakehouse on the other hand, is a new approach that combines the strengths of data lakes and data warehouses. A data lakehouse offers the scalability, flexibility, and cost efficiency of a data lake while providing the reliability, consistency, and governance capabilities of a data warehouse. To this end, an additional organizational and structural layer is added to the data lake to facilitate data management and analysis.
As the volume of data grows exponentially, Data Lakes serve as an essential component of the Data Pipeline.
Advantages of the Data Lake
Using a data lake has the following benefits for your business:
Data Lakes are extremely versatile storage locations for data, as they can hold not only structured, but also semi-structured and unstructured data sets.
This flexibility makes it an indispensable tool for complex analysis projects and machine learning projects.
With a data lake, companies can manage and analyze their data in an efficient and effective way to gain valuable insights and make informed decisions.
Data Lakes offer the advantage that less planning is required up front to ingest data.
In contrast to data warehouses, there is no need for complex schema and transformation definitions. This means that businesses have to deploy fewer staff and can therefore save costs. In addition, the actual storage costs of data lakes are significantly lower compared to other storage locations such as data warehouses.
This enables businesses to more effectively optimize their budgets and resources to successfully implement their data management initiatives.
Data Lakes are an extremely valuable tool for businesses that want to improve their scalability.
Compared to other storage services, they offer impressive total storage capacity and self-service functionality that allows organizations to access and use their data quickly and easily.
In addition, Data Lakes serve as a sandbox in which employees can develop successful POCs. Once a project is proven on a smaller scale, it can be easily expanded to larger scales through automation.
Data Lakes are therefore an indispensable tool for businesses that want to improve their scalability and use their data more effectively.
Reduced data silos
In numerous industries, businesses are confronted with data silos within their organization - be it in the Public health or in the supply chain.
But by implementing data lakes, which take raw data from different functions, these dependencies can be broken. Because there is no longer a single owner for a particular data set, silos dissolve by themselves.
This solution enables businesses to use their data more effectively and gain a holistic overview.
Improved customer experience
A successful proof of concept may not be obvious at first glance, but it can improve the overall user experience and empower teams to better understand and personalize the customer journey through innovative and illuminating analytics.
This advantage is of great value and can lead to a significant competitive advantage in the long term.
It is therefore worth investing in the development of proof of concepts and considering them as an integral part of the business strategy. By creating customized solutions that meet customers' needs, companies can strengthen their customer relationships and improve their brand image.
Data Lake Use Cases
Data Lakes are primarily known for their ability to store large amounts of raw data without the need to define the business purpose from the beginning. The following use cases for Data Lakes exist, for example:
Document Automation with Konfuzio
Konfuzio is an AI-powered document automation platform that uses machine learning algorithms to extract structured data from unstructured documents such as invoices, contracts, and receipts.
Data stored in a data lake can be loaded and analyzed by Konfuzio.
Konfuzio first ingests a document and then extracts relevant data points using its AI algorithms. These data points can include customer names, invoice numbers, and payment amounts, among others. Once extracted, the data can be transformed and loaded into a data lake, where it can be combined with other data sources for further processing and analysis.
By using Konfuzio with a Data Lake, Businesses have the following advantages:
- Streamlining their document processing workflows
- Improve data quality
- Gain deeper insights into their document data
Data stored in the Data Lake can be used for advanced analytics, such as machine learning and natural language processing, to gain insights and identify trends.
Automating documents using Konfuzio and a data lake can be more cost-effective than traditional document processing methods because it can reduce the need for manual data entry and other time-consuming document processing tasks.
Overall, Konfuzio and a Data Lake can provide organizations with an efficient and more accurate approach to document processing, enabling them to process, analyze, and gain insights from their document data faster and more efficiently.
Here you can Konfuzio free trial.
Proof of Concepts (POCs)
Storing data in a data lake is particularly suitable for proof-of-concept projects.
The versatility of the Data Lake makes it possible to store different types of data, which is particularly advantageous for machine learning models. Both structured and unstructured data can be integrated into predictive models.
This is particularly important in use cases such as text classification of Konfuzio of use, since data scientists generally cannot use relational databases for this purpose without first editing the data to meet the schema requirements.
In addition, a data lake can also serve as a sandbox for other Big Data analytics projects. This ranges from developing rich dashboards to supporting IoT apps that typically require real-time streaming data.
Once the purpose and value of the data has been determined, it can then be subjected to ETL or ELT processing to store it in a downstream data warehouse.
Data backup and recovery
Data Lakes offer an attractive alternative for disaster recovery scenarios due to their high storage capacity and low costs.
In addition, they can also be of great use in data audits for quality assurance, as the data is stored in its native format without having to be transformed first. Especially when there is a lack of documentation on data processing in the data warehouse, teams of previous data owners can review the work to ensure that the data is of the highest quality.
Other use cases may include:
- Advanced Analytics: Data Lakes can store large amounts of data that can be used for advanced analytics such as machine learning and data as well as Text mining can be used. This can help businesses gain deeper insights into their data and make more informed decisions.
- Big Data processing: Data Lakes can store large amounts of data and are therefore ideal for processing Big Data workloads. In this way, companies can process data faster and more efficiently and thus make faster decisions.
- Data archiving: Data Lakes can be used to store historical data that is no longer actively used in day-to-day business. In this way, businesses can free up space on their primary storage systems and reduce storage costs.
- IoT Data Storage: Data Lakes can store large amounts of data generated by Internet of Things (IoT) devices such as sensors and other connected devices. This can help companies analyze the data to identify trends and make informed decisions.
- Data Discovery: Data Lakes can provide a single source of truth for all business data, making it easier for analysts to discover and explore new data sources. This can help companies uncover hidden insights and make more informed decisions.
The Data Lake can store data with no immediate purpose, providing a cost-effective way to retain cold or inactive data.
These can later be useful for regulatory inquiries or new analyses. Thus, a Efficient use of storage space ensured and at the same time can valuable data retained for future purposes be