What Is Data Lake Architecture? A data lake is a central repository that stores vast volumes of both structured and unstructured data “as-is” in a native format. Now let’s add a data lake. And since this is a newer term, we will talk about it in more detail. Data lakes, as a way to store unstructured data in a more cost-effective way, began to grow around the 2000s. The key phrase here is profitability.

Data lake example

Going back to the grocery store example we used with the data warehouse; you might consider adding a data lake to the mix when you need a way to store big data. Think about the social sentiment you collect or the results of your ads. All of this is unstructured, but valuable, and can be stored in a data lake and work with both your data store and your database.

• Note 1: Having a data lake does not mean that you can simply load your data willy-nilly. This leads to a huge amount of data, but at the same time, it simplifies the process; and new technologies, such as the data catalog, will continually make it easier to find and use data in your data lake.
• Note 2: If you would like more information on the ideal data lake architecture; you can read the full article that we have written on this topic. It explains why you want your data lake to be built with Object Storage and Apache Spark, not Hadoop. MongoDB is an essential part of an efficient Data-Science UA

Data-Science UA:

We started our discussion by investigating whether Hadoop alone could meet the needs of a Data Lake Architecture and found at least three problems. Can we add another layer of robustness to the architecture to address these challenges while maintaining design principles; using shared hardware with a low total cost of ownership, open-source mode; read mode and Hadoop distributed data layer as before. Are they consistent? I chose the topic of this article because MongoDB is the best database in the data lake for Hadoop only. If you are using another open-source NoSQL database; you will find that there are almost no secondary indexes (using secondary indexes will result in data synchronization); and there is no grouping and aggregation functionality either. You can use some of these databases to write data to the data lake; but if you want to use secondary indexes for flexible reads for business purposes, you cannot. If we want to use an open-source DBMS in a data lake, we have already said that their fixed model; and expensive vertical scaling model are contrary to our data lake design principles. Therefore, it is recommended to use the following architecture to build a data lake.

MongoDB is very important for data lake

This architecture adds MongoDB as a persistent layer to any dataset that needs to express a query; for three reasons you need to index (listed above). Since the demand data comes from consumers, whether or not to publish it to HDFS and/or MongoDB; I recommend using the government function to determine. Whether stored in HDFS or MongoDB, you can run distributed processing tasks like Hive and Spark. However, if the data is on MongoDB since the screening criteria are decentralized in the database; as opposed to scanning files in HDFS, you can perform efficient analysis on time slices of the data. Likewise, the data in MongoDB can also be used to generate real-time low latency reporting; and provide live data platform services to all systems used by embedded applications. Nowadays, some companies simply copy data into Hadoop for transformation and then copy it elsewhere for valuable work. Why not use a Data Lake Architecture to maximize its value? Using MongoDB can double the value multiple times.