A data lake is a data architecture that provides a central repository for large volumes of raw data. The data lake makes this data available for analysis across the enterprise. areto offers Data Lakes with Azure, AWS, Snowflake, Databricks.
A data lake is a data architecture that allows companies to store all their data from different sources at a single point of truth. This can be done regardless of the size, format, or processing level of the data. Data Lakes can store structured data, semi-structured data and unstructured data such as audio, video and social media files. This allows you to create more connections within your data and gain greater insights from the data.
The stored data can be processed directly in the data lake, or with various platform architectures and analytics. A data lake can be used on-premise or in the cloud as a cloud data lake. Data Lakes are particularly suitable for companies that collect and process Big Data. In order to prepare the data for analyses and further processing, additional tools are offered, which can be used as a layer on the data lake. Data Lakes usually form the storage layer in complex data architectures such as a Data Lakehouse architecture or a Data Fabric architecture.
Data Lakes offer large storage capacities at low costs. Using a data lake significantly reduces the complexity of storing data compared to a data warehouse. In addition, a flat hierarchy of the data is ensured. The unprocessed raw data has a high information value, as no important values are lost due to a lack of processing. A storage of the data can take place just like the storage location independently of how and whether this data is needed at a later point in time. Data Lakes realize the real-time import of accumulating data volumes.
A data lake follows the ELT model. Through storage, a simple democratization of data takes place so as to prevent the emergence of data silos. Data Lakes make the data available to all users throughout the enterprise, so that the data can be used for different units and use cases. In addition, data lakes offer high scalability.
The high availability allows your Data Scientists, Developers and Data Analysts to access the data with a wide variety of tools and frameworks. This allows you to perform analyses without having to transfer your data to a separate system.
Nearly infitine scalability
Single point of truth
Enterprise-wide data availability
Basically, Data Lakes consist of three different layers. The basic component is provided by the sources from which data is fed in. These include SaaS applications, IoT sensors and business applications. ETL is used to load this data into the data processing layer. This is scalable and consists of data storage, metadata storage and replication to ensure high availability of data. In addition, the efficiently designed data processing layer includes the administration and security features of the data lake. Business rules and configurations within the layer can be ensured through administration. After the processing layer, the data lake makes the processed data available to the target applications. This is done via connectors or an API layer. The data can then be used for analyses, BI applications and visualization tools. These include, for example, Tableau, MS Power BI, SAP Analytics Cloud (SAC) and many more. Models for ML and AI can also be fed with the data from the data lake.
aretos reference architecture for your data lake using Microsoft Azure, Snowflake, Datavault Builder, Databricks und Power BI.
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Snowflake’s Data Cloud Platform offers a hybrid approach that combines the benefits of a data lake with the advantages of a data warehouse and cloud storage. Snowflake acts as a centralized data store for your business, offering high performance, relational queries, governance and security features. You can use Snowflake’s Data Cloud Platform as a data lake or combine it with cloud storage from Amazon S3 or Azure Data Lake. Also accelerate your data transformations and analytics and take advantage of near infinite scalability. Snowflake Data Cloud Platform is offered as a SaaS solution and requires no hardware or maintenance from you.
Read more about the Snowflake Data Cloud Platform here!
Delta Lake from Databricks is an optimized open source storage layer that provides reliability, security and performance for your data lake. It supports streaming and batch operations. Prevent data silos from happening with Delta Lake. Store and integrate all your data at a single point of truth. Delta Lake supports real-time streaming so your organization can always work with the latest data. ACID transactions and schema forcing are supported, giving you reliability and performance while maintaining the cost model of a Data Lake. You can run data projects directly on the Data Lake and scale them across the enterprise.
Delta Lake serves as the foundation for storage in the Data Lakehouse platform, which gives your organization full control and flexibility to integrate new tools and systems.
Read more about Azure Databricks here.
Microsoft Azure Data Lake Storage reduces the complexity of storing and capturing data. It includes all the capabilities your organization needs to easily store data in all formats. So your developers, data scientists, and analysts can access data of all sizes and speeds across the enterprise. Azure Data Lake supports batch, streaming, and interactive analytics to store your data even faster. Data transformation programs in U-SQL, R, Python, and .NET are supported without requiring you to manage additional infrastructure.
You can connect and extend your existing architecture and operating storage with the Microsoft service. So can your IT solutions for management and security. Scale Azure Data Lake to your business needs and increase productivity by fully leveraging your data assets.
Amazon Web Services (AWS) provides the Simple Storage Service (S3) as the foundation for storing your data in the Data Lake. This allows you to tap into AWS analytics services and frameworks ranging from data collection, data movement, business intelligence, and machine learning applications. Your developers and data scientists can work directly with the data without having to move it first. Amazon S3 gives you high availability, near infinite scalability with key compliance and security features. The serverless data integration service, AWS Glue, allows you to import large amounts of data in real time or in batch from the original sources and move it to your data lake. In addition, AWS Glue provides a centralized data catalog to better understand your data. AWS Lake Formation enables simplified management of your Data Lake.
Read more about Amazon Web Services here.
Read more about Amazon Web Services.
AWS offers a number of other optimized tools such as AWS Athena for data analytics. Connectors to third-party applications are also available to give you the best possible price/performance ratio for your data lake needs.
When using a data lake, there is a risk that it will be overloaded with information assets and data captured for uncertain use cases. This increases the risk of a data swamp, where data cannot be effectively queried and used even though the necessary data is available. To address this risk, data pipelines, queries, data provisioning and updates, etc. can be automated to ensure rapid value creation.
Through best practices, the tools deliver high-quality code and are easy to use through point-and-click graphical user interfaces. By leveraging data lake automation tools and areto’s expertise, you can reduce time to value so you can focus on your business tasks while realizing cost benefits. Use the tools from Wherescape, Matillion, Datavault Builder and areto Data Chef.
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