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Database, Data Warehouse, and Data Lake

There are many different types of data storage nowadays, but we still use the same common known database among a variety of different data collectors. And it still appears to be necessary and widely used.

Additionally to the traditional databases, many kinds of systems can store data. Among them are data warehouses, cloud storages, and data lakes. There are distinctions between these definitions in use-case. Over time, we will find them out. If you wish to know more about ML basics, check this AI blog.

What is a Database? What are the significant components of the database?

Firstly, let us state what exactly databases deal with. database is considered to be a digital storage for data as a structured set of records. The classic 2-dimensional database structure organizes the figures in rows and columns. So, this is an organized collection of data in the form of a table with a unique identification number for each record and the name related to each of the features collected as the column. 

The management software can be tailor-made or standard to operate with the data. Both are called DBMS (database management systems). DBMS provides the database users with the security of data storage, sampling, and updates and helps with data recovery.


Databases can be used to collect different information (people, places, things). This information is typically structured so that the users can sample the necessary features and objects for analysis. Depending on the kind of database, it is used for different purposes and in different cases. Mainly, it is used for drawing info from data sets (Document database); observing information; looking up for information (Key-value stores); analysis of interrelations between data (Graph database).

Suppose it’s necessary to analyze databases according to the scaling principle. Here is a subdivision into those management systems and frameworks that can operate unstructured data and those that cannot.

Cassandra is a free and open-source, distributed NoSQL database management system designed to handle large amounts of data.

The databases that can operate unstructured data: are Cassandra (Column-oriented), CouchDB (Document-Oriented), Vertica (Column-Oriented), and Hbase (Column-oriented).

The data management systems that cannot operate unstructured data: are RDBMSs (Relational), MySQL (Relational), and some others (These are only “C + A”(Consistency + Availability) databases).

One of the disadvantages of DBMS is its high cost because of the interconnection between computing and storing. Furthermore, there is low security of data because of constant deletion. If all these details are not helping you to solve your own ML-related problems, ask a professional advice from AI consultants.

CAP theorem

There is a general principle of operation of all databases. This principle is presented through the properties of databases. It can also be considered through a theorem called the CAP theorem.

The theorem CAP is a heuristic proposition that in any implementation of distributed computing, it is possible to provide no more than two of the following three properties:

1. Consistency of data (equally the same outlook of the data for all clients);

2. Accessibility (Availability for a client to read and write data on an ongoing basis);

3. Partition Tolerance (safe from network problems and operates no matter what);

There are some exemplary data storage applications. These are: Cassandra, CouchDB (A+P); Vertica, MySQL, RDBMSs (C+ A); HBASE, Redis, Hypertable (C + P)

Differently designed databases have different managing models. One more significant feature by which databases can vary is the data structure.

HDFS versus Amazon S3

Let’s make a mini-review on the attempts to improve databases; these attempts are viewed as a battle between HDFS (and Hadoop) and Cloud storage (AmazonS3).

1. HDFS possesses the main issues of the data store of the Hadoop Cluster.

2. HDFS has difficulty with storing large volumes of not big files. Hadoop Cluster prefers a small number of large files (vice versa). In another case, the Name node is overloaded because it demands too much namespace.

3. High cost of processing large quantities of data. Furthermore, the data is recorded inwards and outwards on the disk ;

4. HDFS has a low processing speed because of writing and reading metadata. On the one hand, this is an advantage of a database to contain and show additional detailed information. On the other hand, this isn’t good because it is very time-consuming. What is easier for you? – when you look for your keys in a box, filled with stuff, or in a key-keeper? The answer is obvious.

5. No chance for file modification

6. Too much segmentation (segmentation of data into blocks + replication)

7. Not very secure

As for the Amazon S3, let’s compare it with HDFS. We can see that it is:

– Not expensive (cost-effective);

– More scalable

– Not a file system, but an object system (deals with object info instead of metadata );

– Faster (no need to waste time on

– Has a secure environment (durable)

Generally saying, the S3 is a permanent and trustworthy and not expensive storage service.

At the moment, the cloud storage in the form of Amazon S3’s distinctive features is greater scalability, the ability to store large quantities of data, and cost efficiency. At the same time, the Hdfs use numerous nodes for data storage, which can be much more inconvenient and expensive.

As a result, databases cannot be built on S3, while Data lakes can.

How can we define a Data lake?

The first thing to state is that the Data lake consumes all data attributes. You can store text, images, different files, etc. And Data lake doesn’t need ETL tools.

Data lake uses the platform Amazon Simple Storage Service (S3).

Like a deep bottomless box, Data lake allows users to avoid limitations and gain more freedom in their actions + greater data availability. One can always get back to the needed data and read it, apart from the database. But this fact doesn’t guarantee security. The data lake has its disadvantages, dealing with chaos (too much data of different sorts).

Use case

A data lake is a good decision for companies that store enormous amounts of data (sometimes in petabytes). In this case, a Data lake prevents a company from cost loss and provides simplicity of scale.

What is the definition of a Data Warehouse?

Data warehouses are digital storage locations where data integration takes place.

DW uses ETL (extract, transfer, load) tools to process needed data.

Use case

A DW can be useful for analytical processing, reporting, data mining, and BI. DW helps companies to gain efficiency. It is also suitable for data-driven decisions.

Apart from a Data lake, a Data warehouse has a specific purpose and sphere of use and consumes definite attributes.

And what is the difference between these three definitions?

One should understand the distinctive features of DB, DW, and DL to define a Data lake:

So, a DB is a storehouse with organized data given columns, rows, tables, etc. Databases can deal with structured or unstructured data.

When it comes to Data warehouses it has a unique role. Data Warehouses use Databases to consume information and analyze it (there is a specific layer).

In difference from the database, a Data lake is storage that deals with both kinds o data, whether structured or unstructured. It can also store raw data. The Machine learning models, retime data, AI, and analysis are available in the Data lake (+ many other types of data can be stored in large amounts: video, image, text, etc.). In addition, open data format and cost-effectiveness.

Briefly saying, a data lake deals with many sorts of information; 

Data Warehouse consumes specific info necessary for the analytics of the particular case; Database structuralizes data and does it in a definite order by keeping all the records in table form.

All three systems may seem to perform the same function. It’s not like that. With a closer look, we can see the peculiarities of each system’s role. Moreover, they have pros and cons and are used for different purposes. Thus, we cannot say that they can substitute one another.

Uneeb Khan
Uneeb Khan
Uneeb Khan CEO at blogili.com. Have 4 years of experience in the websites field. Uneeb Khan is the premier and most trustworthy informer for technology, telecom, business, auto news, games review in World.

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