Data Lake is a game-changer for data storage and analytics, allowing companies to store, manage and analyze vast amounts of data in various formats. Imagine having Enterprise-wide access to all of your data, and being able to integrate that data seamlessly with modern analytics and AI tools. That's the power of Data Lake. Whether you want to gain insights from customer data, improve product development, or increase operational efficiency, Data Lake can help you every step of the way. With its flexibility, scalability and cost-effectiveness, Data Lake is the future of data-driven decision-making.
What Is a Data Lake?
Data Lake is a centralized repository that can store almost any type of structured, semi-structured and unstructured data. It can store, process and provide security to data of almost any size in nature.
To understand what is a Data Lake, let’s start from the smallest piece of a data in organization. Consider a company’s daily data. This probably includes transactions and lists which all go into the database of a company. The database is a flexible and detailed storage for your real-time data. Typically, databases store data in tables. Now moving a step further, your data can’t just pile up in a database. That’s where data warehouses come into play. These are a lot more structured systems that act as an archive almost. Data from the databases are stored in rigid systems and are generally summarized here. This helps companies with analytics. Now, while these seem like very organized systems for data, not all your data can be stored so tightly. You might have heard of data lakehouses. A data lakehouse is a new data management architecture that integrates the features of both a data lake and a data warehouse.
The definition of a data lake is storage for almost any type of raw data. Here, large amounts of data in various forms - files, tables, images, videos etc. of any sizes can be stored. Data Lake allows you to store any type of structured, semi-structured and unstructured data at any scale.
Data Ingestion
This refers to how data is collected and brought into a data lake. Since data lakes can store structured, semi-structured, and unstructured data, it processes data in a specific way. One of these methods includes batch processing. This is when a computer can fulfill several tasks at once in a “batch” without any user interaction. Batch processing is an automated part of periodically moving data into the data lake. Another way data is processed is through stream processing. This process – also called real-time analytics – can be used to process the data as it’s being received. It continuously analyzes the data stream. The last one that we’ll cover is the Internet of Things data. This is the data generated from the multiple connected devices, networks, and software connected to the internet. As you can expect, the data found here is vast and varied. This makes a data lake the ideal storage option for IoT data.
Data Pipeline
A data pipeline allows for the movement and transformation of raw data. Essentially, it’s what allows batch processing and stream processing to occur. Data can come from APIs, SQL or NoSQL databases, files, and more. However, this doesn’t mean they’re ready for use. Data will sometimes first undergo processing such as filtering, masking, and aggregations. The data pipeline ensures that the data securely moves from one place to another in a controlled and secure manner.
Data Lake Architecture
The key components of a data lake include data storage, data processing, and data access capabilities. Sangfor’s Nano Cloud that is built for Small and Medium Enterprises is one of the examples, where we use Data Lake concept to collect and store the raw data.
Data Storage
After ingesting data and being collected, the data needs to be adequately stored in a data lake. Through Sangfor’s platform, all resource requirements are met with Hyper-Converged Infrastructure appliances and switches. This allows for a unified visual management system.
Data Processing
This takes place in the “pipelines” before data reaches the data lake. It includes any filtering or transformations before the data can be added to the lake.
Sangfor’s HCI solution ensures that a single unit provides up to 100,000 IOPS (download HCI brochure for more details) and supports linear expansion. This means you get peak performance with no bottlenecks.
Data Access
The point of the data lake is to improve user access and allow several people to access the raw data as needed.
The Sangfor architecture is fully redundant to ensure maximum business stability. You’ll never experience any data loss - even if the hardware fails. The XDDR solution also uses a coordinated response to contain and mitigate breaches when they happen.
Security for Data Lakes
Due to the large and unstructured nature of a data lake, it can be difficult to ensure adequate security. Here are a few best practices to ensure the safety of your data lake:
Data Encryption
Naturally, the data in our data lake should be secure through any means. This means setting up encryption and monitoring for sensitive information.
User Access Control (UAC)
User access can be a difficult issue for data lakes because of the sheer amount of information and channels to get in. Try to create a standardized access control system that can easily track and limit access and use of data.
Regular Backups
Ensure that the data is continuously backed up and in safe hands.
Data Governance
This involves the policies, auditing, and visibility of the data in your data lake. Try to classify your data in catalogs within the data lake and ensure that employees understand their boundaries. Ensure regular compliance through auditing.
Some main advantages of data lakes include:
- Ability to import any amount of data in real time.
- Highly scalable.
- Improves customer relations through social media analysis and more.
- Improve research and development within the company by providing an ideal test field.
- Allows you to store and analyze machine-generated IoT data to improve business efficiency.
- Broader ranges of data can be accessed a lot faster in their raw states.
A few disadvantages of using a data lake include:
- Reliability issues when it comes to combining different types of data and more.
- Slow performance as data increases in the lake.
- Lack of proper security due to low visibility and other limitations.
Refer to our another article on Data Lake vs Data Warehouse where we have mentioned the advantages and disadvantages in details.
A Sangfor Data Lake Example
Sangfor’s case study with the Kweichow Moutai Group displays a perfect example of Sangfor’s data lake capabilities. After choosing to go digital in 2017, the company decided to construct a Hyper-Converged Server Resource Pool and Network Security System with the help of Sangfor. This venture would help realize the goal of "Smart Moutai" and revolutionize the business. Sangfor’s Hyper-Converged Infrastructure resources were used to create the pool – or data lake – and effectively improved the Kweichow Moutai Group’s IT posture. It reduced operational costs and energy consumption while the virtual architecture ensured unlimited expansion. The network security features also enhanced the security to achieve centralized information sharing and strategic linkage for the business.
Sangfor offers Data Lake, Data Warehouse for any kind of large data stroage requirements for enterprises. Visit Sangfor aStor page to know more or contact us for more details.
