Data Lakes: Definition, Benefits, and Challenges

A data lake is a concept that has generated a lot of attention in today's storage systems. It is NOT the same as a data warehouse, either. Many people would not be familiar with the term "data lake," thus they would be looking for the definition. However, this term must have been known to those involved in data practice before.

 

Data Lake is a new tool used by businesses that produce and manage vast amounts of data for operations and machine learning projects. Infinite amounts of data are managed and organized with it.

 

Companies are increasingly storing their company data in data lakes. Data pandemonium might, however, result from storing a lot of data in different formats! Let's examine the benefits and drawbacks of data lakes.

 

 

What is a Data lake?

 

Imagine a Data Lake as a sizable container that acts as a storage repository and has the capacity to store significant volumes of data in many types, including unstructured, semi-structured, and structured data. It is a location where any type of data can be ingested in its original format without any set restrictions on account size or file size.

 

Big data from numerous sources are kept in a raw, granular format in a data lake, which is a central storage repository. Data can be preserved in a more flexible format for future use because it can be stored in an organized, semi-structured, or unstructured manner. A data lake marks the data it stores with identifiers and metadata tags to speed up retrieval.

 

Data collection and use are both constantly expanding. According to digital projections, use is expected to exceed 4.5 trillion gigabytes by the year 2022. However, the issue is that none of the data is structured. The fact that around 90% of the data is unstructured or semi-structured makes data management extremely difficult. Here, Data Lake is put to use. Big data can be stored in the raw form that it was originally created in using data lakes.

 

Data lakes do not require pre-processing of the data, in contrast to data warehouses. Another affordable method of storing data is through data lakes. Business analysts are the primary users of data from warehouses, whereas data scientists, data developers, and business analysts can all use data lakes.

 

Modern data lakes, as opposed to conventional ones, which rely on on-premise storage facilities, offer affordable data stores that are expandable by keeping their data in the cloud. A cloud-based analytics layer that enhances query performances against data in a data warehouse is typically present in modern data lakes. Analytics will be more effective as a result.
 

Organizations wishing to collect all the data from many data sources in one location to gain insights from it can find answers in data lakes. Data lakes are an additional analytical support tool since they enable business intelligence (BI) tools to directly access data as needed.

 

Also Read | What is ETL and How does it Work?

 

 

What Distinguishes a Data Lake from a Data Warehouse?

 

The Key differences between Data Lakes and Data Warehouses are:

 

• Data Processing:

 

There are two distinct methods for storing data: the data lake and the data warehouse. A data lake accepts data in all three states—structured, semi-structured, and unstructured—in contrast to a data warehouse, which only accepts data in a structured form. Multiple data warehouses can be easily created from the data lake because it stores data in many shapes and sizes.

 

 

• Data Source:

 

Data from specified sources are only stored in organized data warehouses. On the other hand, the data lake contains data from any sources in any format in structured, semi-structured, and unstructured states.

 

 

• Data Retention:

 

For huge data quantities, a data warehouse is comparably more expensive than a data lake. In the data warehouse, queries run more reliably, quickly, and efficiently. However, the data lake's query results are getting better.

 

 

• Agility:

 

Data lakes are extremely adaptable and may be set up or changed. Less so are data warehouses.

 

 

• Security:

 

Because data warehouse concepts have been around for a longer time and are better developed now, they are typically more secure than data lakes. whereas the data lake security approach has room to grow.

 

 

• Use of Data:

 

A data warehouse is appropriate for business users and is used for operational reporting. In contrast, data scientists are using data lakes for advanced analytics.

 

Also Read | Data Lakes vs. Data Warehouse: Definition & Differences

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Benefits of Data Lake

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Architecture for a Data Lake:

 

The initial architecture for traditional data lakes was intended to store and process the data, but it had significant restrictions. Without completely embracing the cloud, traditional systems like Hadoop offer an on-premise environment.

 

The initial iterations of data lake systems required human resource allocation, capacity planning, data performance improvement, and other tasks. Traditional architectures were the only choice open to businesses prior to the advent of data cloud lakes.

 

Large amounts of data are produced by organizations as a result of their operations, activities, and customers. To increase client retention rates and attract new ones, data scientists can examine the collected data.

 

Data lakes are essential because they offer a platform where data may be stored effectively, reliably, and conveniently for analysis given how "large" that data can become. The need for data storage grows in importance and demand as technology develops further. Data lake architecture can help with it.

 

The following essential elements make up the data lake architectural model, which makes it reliable and efficient:

 

1. Security:

 

Data lakes need strong security in the age of growing cyber threats to stop data breaches that could lead to data theft. Organizations should implement security measures including multi-factor authentication, role-based access, and authorization.

 

 

2. Government:

 

It is important to streamline the sanctity of the entire data intake, preparation, categorization, integration, and query acceleration process. This will make sure that when necessary, data lake upgrades can be implemented.

 

 

3. Processes for monitoring and ELT:

 

In case any data transformation is necessary, one should set up organization tools to coordinate data flow from the raw layer to the cleansed layer to the sandbox and application layer. The organization, the owners, or anybody else in charge of the data and data lakes is given supervision.

 

 

The architecture of a data lake cannot be characterized by a single formula. Three key data lake architectural ideas do exist, though:

 

1. No matter if the data is structured or not, it is all loaded into the data lake.

2. The data lake stores the information in either an incompletely converted or unconverted state.

3. The data is transformed and fitted into a schema in accordance with the business requirement.

 

 

Different Layers of a Data Lake:

 

The different layers are:

 

• Ingestion Layer: 

 

The Data Lake Layers' First Step is the ingestion layer. It collects information and adds unprocessed data from multiple sources to the data lake. Raw data is prioritized and categorized by the ingestion layer.

 

 

• Distillation Layer: 

 

The distillation layer takes the data from the ingestion stage and turns it into structured data so that analysis may be done more effectively and easily. In this step, data preparation gets started. For effective analysis, the distillation layer purifies and changes the intake layer's raw data.

 

 

• Processing Layer: 

 

The processing layer executes interactive, real-time, and batch user queries as well as analytical tools on structured data.

 

 

• Insights Layer: 

 

The query interface is represented by the insight layer. Data analysis can make use of SQL or NoSQL queries.

 

 

• Unified Operations Layer: 

 

System management and monitoring are under the control of the Unified Operations Layer. Both data management and workflow management are done by it.

 

Also Read | What is Data Lineage? Business Value of Data Lineage

 

 

Role of Data Lakes:

 

The architecture of data lakes is flat; there is no hierarchy of files or folders. Each item of data in a data lake has a unique identifier and is tagged with a set of metadata. Data lakes draw from a wide range of data sources, including websites, corporate applications, IoT devices, and mobile apps.

 

By consolidating all data into a single area rather than storing it in several data silos, data lakes enhance the functionality of enterprises. Although some data lakes are located on-site, the majority are located in a cloud storage environment. Cloud-based data lakes are hosted by data storage service providers so that businesses can process data as needed. By transferring particular raw data to specialized data warehousing systems for processing, data lakes can feed into a data pipeline.

 

 

Data Lake: Positives

 

Data lakes are better suited for analyzing data from various sources, particularly when the initial cleaning of the data is time-consuming or difficult.

 

Here are a few of a data lake's key benefits.

 

1. Volume and Variety:

 

Big Data, artificial intelligence, and machine learning all demand a lot of data, which a data lake can support. Data lakes are capable of handling the volume, diversity, and velocity of data being absorbed from numerous sources in any format.

 

 

2. Speed of Intake:

 

During ingest, the format is unimportant. It employs schema-on-read rather than schema-on-write, delaying data processing until it is actually required. Data is easily written.

 

 

3. Lower Expenses:

 

In terms of storage costs, a data lake may be considerably less expensive than a data warehouse. This enables businesses to gather a greater range of data, including unstructured data from social media, email, rich media, and Internet of Things (IoT) sensors.

 

 

4. Greater Accessibility:

 

When data is kept in a data lake, it is simple to open copies or portions of the data so that other users or user groups can access them. Companies can offer expanded accessibility while controlling data access.

 

 

5. Advanced Algorithms:

 

Data lakes enable businesses to use deep learning algorithms to identify trends and run complicated queries.

 

Also Read | Top 10 Big Data Technologies

 

 

Data Lake: Drawbacks

 

Data lakes can potentially have significant drawbacks, depending on your needs for data processing and storage. The following are the disadvantages of employing a data lake in organizations:

 

1. Complex On-Premises Deployment:

 

A cloud data lake can be spun off with ease. On-premises data lake deployment can be considerably more difficult. Although there are on-premises options like Hadoop or Splunk, data lakes are designed for the cloud.

 

 

2. Learning Curve:

 

A data lake comes with a small learning curve, new tools, and new services. This necessitates hiring team employees with data lake skill sets, hiring outside assistance, or training.

 

 

3. Migration:

 

Moving from a data warehouse to a data lake requires careful planning of your data strategy to handle your data sets if you are already using a data warehouse. This may be hard depending on your infrastructure.

 

 

4. Handling Queries: 

 

While a data lake can quickly and easily absorb data, it is not as query-optimized as structured and semi-structured data in a data warehouse. Data retrieval is not as simple as it is with a data warehouse, but using best practices for database queries can help. The Extract, Load, and Transform (ELT) process is used in a data lake to handle data after it has been loaded.

 

In the absence of governance, semantic consistency, and access constraints, the data lake risks becoming a "data swamp" of unprocessed, unusable data.

 

Also Read | What is Hive Big Data and its Benefits?

 

 

To summarise:

 

Business intelligence users are enthused about data lakes because they can handle diversity and velocity. There is now a chance to integrate processed data with arbitrary data that is available online. On-premises and cloud-based data lakes are expanding as the use of big data increases.

 

To identify illness causative patterns, one can sort through machine data such as X-rays and MRI images. Large amounts of sensor data may be processed very quickly in IoT applications. The retail sector can provide an omnichannel experience thanks to a wealth of user-specific data.

 

Data lakes are helpful for both routine corporate reporting and sophisticated predictive analytical applications, particularly when combining diverse data formats.