The term Data Lake comes across by James Dixon, the chief technology officer, which means the ad hoc nature of data. He argued that data marts have inherent data silos problems that the data lake could end while promoting data lake. But unfortunately, the term data lake is often deprecating as a marketing label for those products that support-oriented object storage-Hadoop.
What is Data Lake?
A data lake represents a storage system where a considerable amount of raw data from many sources is stored in a raw, granular, or native format. Data lake stores structured, semi-structured, unstructured, or binary data for various purposes.
When held, a data lake is associated with the metadata tags, which helps faster retrieval. A data lake is established within the business or organization’s data center or using the Google and Amazon cloud services. These are generally composed of scalable and economic commodity hardware bundles or clumps. It is essential for all those companies or organizations which warns to take full advantage of their data.
A data lake example is that many companies like Amazon S3 use cloud storage or a distributed file system.
What is Cortex Data Lake?
Cortex data lake represents a storage system that collects, integrates, and normalizes your enterprise’s security data. The main goal of the cortex data lake is to identify and stop sophisticated attacks using advanced artificial intelligence (AI) and machine learning across all your enterprise’s data.
A cortex data lake is a cybersecurity system. Palo Alto Networks® Cortex Data Lake “provides cloud-based, centralized log storage and aggregation for your on-premise, virtual (private cloud and public cloud) firewalls, for Prisma Access, and for cloud-delivered services such as Cortex XDR”.
Difference between data warehouse and data lake
Data lakes and data warehouses are much different from their database, and data warehouse is both the various strategies to store data. The difference between them could easily be understood as that lake is liquid, primarily unstructured, created or fed, or dependent on the other unfiltered water sources like rivers and streams. On the other hand, the warehouse is made by humans. It has shelves destined places for the things inside it, and it is prestructured while data lakes are not.
A data warehouse allows the strategic use of data. It collects data from internal and external sources and optimizes it for business purposes. The data schema is present in a data warehouse, which means a plan for data into the database upon its entry. It handles only structured data and has a preplanned schema, whereas it is unnecessary for the data lake and can house structured and unstructured data. Even it does not have a preplanned schema for the data it houses.
Data lakes are less secure than the data warehouses as data warehouses existed for a more extended period, and therefore security methods have time to mature.
Data lakes generally receive relational and nonrelational data from mobile apps and social media. Data warehouse receives online transactions processing applications to support business sales and inventory teams.
A data lake is typically used when an organization needs a repository of data and can afford to apply schema on its access. In contrast, data warehouses are more useful when a massive amount of data needs to be readily available from the operational systems.
A data lake is highly agile, while data warehouses are less so.
The data lake isn’t a single or specific technology. Instead, there are many more technologies that delegate them, and some of the traders that offer these technologies are Amazon, which offers AmazonS3 with limitless accessibility; podium which offers accessible and suite management features, Oracle, which offers Big Data Cloud, Apache that offers Hadoop-the open source ecosystem which is one the most used data lake services.
Data lake advantages
It is cheap and stores raw data for low cost to implement as the technologies used to monitor the data lake are installed on low-cost hardware and are all open sources like Hadoop.
-There is no inherent data structure in the data lake, which means that any user can easily access the data lake data.
-It is flexible as it allows the data to be in its native format
-It supports the various level of investment users as access is possible for all the users.
-The data lake is scalable as it lacks the structure, and it is highly agile, too, which allows different types of methods to interpret data, SQL queries, etc.
Why do data lake projects fail?
Data lake projects fail because the data lake might turn into a data graveyard as if any organization, which due to some reasons, practices poor data management, can lose data track in the lake no matter how much it is poured in. The other drawback is that the data lake may not provide accessibility in practical use to the organization; hence it is essential to maximize the investment and reduce the risk of failed deployment in the data lake.