What Is a Data Lake?
Data lakes are scalable repositories of raw data that can include structured, unstructured, and semistructured data from many different sources. For example, a data lake can include JSON files, CSV files, PDFs, multimedia, and data from mobile apps, IoT devices, text, or social media. The data can be current or historical, with the intent that an organization will mine the data for insights.
Data lakes typically use the extract, load, transform (ELT) approach to data ingestion, whereby data scientists or analysts will only structure the data as needed. Data is taken from a data lake and structured to support online analytical processing (OLAP) systems. This differs from online transaction processing (OLTP), wherein databases with fixed schema or structures support real-time transactions for processes, including e-commerce, record keeping, and consumer or business apps.
Benefits of a Data Lake
Any advanced analytics implementation, such as for energy sector resource analysis, life sciences research, and smart cities planning and development, is better with a data lake. Because data lakes are effectively vast repositories of unstructured data, they are easy to manage and maintain, and users can scale up storage cheaply and independently from compute resources. To make the best use of data lakes, organizations will need the expertise of data scientists, data engineers, and AI architects to sort through the vast variety and quantity of data to generate actionable insights. But because the data types can include multimedia and other unique forms of information, the quality of insights can be more nuanced and incisive.
Summary of Data Lake Benefits
Choose data lakes when you have a vast quantity and variety of data sources and have the expertise of data scientists, data engineers, and AI experts to extract the most value from your data.
- Unstructured data supports more varieties, including multimedia.
- Data storage can scale cheaply and independently from compute resources.
- Although data lakes require diversified expertise to mine insights, the quality of insights can be unique and more nuanced.
What Is a Data Warehouse?
Data warehouses are similar to data lakes in that they support OLAP processes, but they differ by housing data in a fixed structure or predefined schema. Subsequently, data warehouses cannot support complex data types like multimedia, and the data must be cleaned prior to ingestion via extract, transform, load (ETL) processes. This data preprocessing step means that data warehouses are generally more costly and effort intensive to maintain and manage and do not scale as efficiently as data lakes. However, because the data is more curated, it is easier to connect data warehouses to automated business intelligence (BI), visualization dashboards, and OLTP services. Businesses aren’t as dependent on data scientists, AI engineers, or AI architects to mine the data for insights.
Benefits of a Data Warehouse
Data warehouses are ideal for historical data analysis, measurement of marketing campaign effectiveness, transactional reporting, and on-demand queries. Because of a data warehouse’s highly structured nature, data analysis is more straightforward, and the data warehouse can be connected directly to BI tools. The operating budget for a data warehouse may be higher than a data lake, but organizations will likely need fewer doctorate-level specialists on staff to mine the data for insights.
Summary of Data Warehouse Benefits
Choose data warehouses when your data sources are more defined and provide fewer data types that fit into fixed structures. Data warehouses will be the most efficient when connected directly to BI tools and dashboards, forgoing the high cost of data scientists and data engineers.
- Structured data is more narrowly defined.
- Data analysis is more straightforward.
- Insights are more readily available via traditional BI tools.
Data Lakes, Data Warehouses, and AI
AI and machine learning (AI/ML) are making data lakes and data warehouses more essential as both AI/ML and deep learning require high volumes of data to be effective. Data lakes are cost-efficient ways to store high volumes and varieties of data, and data warehouses in the cloud are increasingly using AI/ML capabilities in their software. For example, financial institutions can use AI analytics in combination with data warehouses to monitor for irregular patterns and help detect and prevent fraudulent transactions.
AI/ML and deep learning replicate the complex decision-making normally carried out by data scientists to recognize patterns in the data that forms the basis for predictions and insights. When AI is combined with data lakes or data warehouses in a complete data analytics pipeline, businesses can accelerate time to insight and value and scale operations more efficiently than with human operators alone.
Data Lakehouses: Making Data More Available for AI/ML
A new modality called a data lakehouse is emerging, and it combines the flexibility of a data lake and the semistructured nature of a data warehouse to make data more available for AI/ML. In the current state of the industry, file system standardization—the backbone of data lakes and warehouses—has the potential to drive more widespread adoption of data lakehouses for AI/ML, but it may take a few years for standardization to reach maturity.
Intel® Technologies Empower Data Lake AI and Data Warehouse AI
Most of the world’s AI data pipelines are already running on an installation base of Intel® Xeon® processors.1 Data lake and data warehouse infrastructure, including enterprise applications such as Oracle, SAP, and MS SQL Server, are already optimized for Intel® hardware. Intel is constantly working with ecosystem providers in the AI, compute, and data storage markets to introduce new, advanced AI/ML and deep learning capabilities that help speed up data processing even further.
Built-In Accelerators in the Latest Generation of Intel® Xeon® Scalable Processors
The 4th Gen Intel® Xeon® Scalable platform integrates several analytics accelerators, called Intel® Analytics Engines, directly onto the processor die. The CPU offloads data movement, compression, and encryption workloads to the integrated accelerators to free up CPU cycles for more AI and data analysis processing.
- Intel® In-Memory Analytics Accelerator (Intel® IAA) accelerates queries per second with in-memory data compression, saving memory bandwidth per query vs. software optimization‒only solutions.
- Intel® Data Streaming Accelerator (Intel® DSA) moves data fast between CPU memory and cache and attached memory, storage, and network devices.
- Intel® QuickAssist Technology (Intel® QAT) speeds encryption and data compression for bulk data storage.
The net result is fast insights in existing data analytics pipelines, with simpler configurations and less dependence on external accelerators.
Intel® FPGAs Drive Fast Data Compression and Encryption
Intel® FPGAs can enhance a data lake or data warehouse deployment by accelerating data compression and encryption workloads and offloading these workloads from the processor to free up cycles for analytics processing.
Intel® Developer Tools and Open Source Libraries
For developers who need to architect and maintain data lakes and data warehouses, Intel offers several developer tools and open source libraries that are built to handle high volumes of data while offering optimized performance on Intel® hardware.
Choosing Infrastructure for Data Lake AI and Data Warehouse AI
Whether you build or manage a data lake or data warehouse, Intel® technology can help improve the speed and efficiency of data pipelines with built-in AI acceleration.