What is a Data Lake?
Data Lake is a storage repository which can have a large amount of raw data like structured, semi-structured and unstructured data respectively. The function of a Data Lake which has to store data doesn’t have any fixed limits or any specific data type on account size or in a file. By this feature, the outcome gets high due to its feature of high data storage quantity which increases the performance.
The word ‘lake’ emphasizes that you have multiple streams of the river coming in; a Data Lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
Data Lake is an effective way to store all kind of data of an organization for a later process. Analyst such as based on deep science can focus on finding patterns.
The Data Lake is a flat architecture which is diverse from a data warehouse as it follows a hierarchal structure where data is stored in Files and Folders. The data elements in a Data Lake has given a unique identifier and tagged with metadata information.
Why Data Lake is Important?
- You must have heard about software library called Hadoop. It is a framework store and manages the Big Data, an application which runs on a clustered system. This framework also can be stored in a Data Lake which is very easy as it does not need any enterprise model data for the Data Lake.
- It can handle larger data volume, data types, and metadata.
- For business purpose and solution, Data Lake works effectively.
- It is highly helpful for working under Machine Learning and Artificial Intelligence by app development companies.
- The data arrangements don’t need any specific structure called silo. It works on every aspect view of customers.
- As per forecasting the Data management market size, it generated revenue over 42 billion U.S dollar in the year 2018 and still, it’s progressing.
How the Architectural Process is formed for Data Lake?
- It consists of 3 sources. These are real-time ingestion, micro-batch ingestion, and batch ingestion. It has two similar ingestion level blocks for functioning –
- Ingestion level 1: It carries the source side to loading data.
- Ingestion level 2: It carries the SQL, NoSQL queries or even excels for analyzing the data from the system.
- Hadoop distributed file system level: HDFS is a storage process which is an effective way for both structured and unstructured data.
- Distillation level: It takes data from the storage level and converts it to structured data to make analysis easier.
- Processing level: It runs analytical algorithms and user’s queries with varying real-time, interactive, batch to generate structured data for easier analysis.
- Unified operations level: It governs system management and monitoring. It also includes auditing, proficiency management, data management, workflow management.
Description of each Architectural Process of Data Lake
1. Data Ingestion
Data Ingestion allows getting data from different data sources and loading into the Data Lake.
Data Ingestion enhances:
- Types of Structured, Semi-Structured, and Unstructured data.
- Multiple ingestions level like Batch, Real-Time, One-time load.
- Various types of data sources like Webservers, Databases, IoT, and FTP.
2. Data Storage
Data storage should be scalable, effective and should have access to data exploration. It supports different data formats too.
3. Data Governance
Data governance is a process of managing availability, usability, security, and integrity of data used in an organization.
Security needs to be implemented in every layer of the Data Lake. It starts with Storage, Unearthing, and Consumption. The basic need is to stop access to unauthorized users. It should support different tools to access data with easy to navigate GUI and Dashboards. Authentication, Accounting, Authorization and Data Protection are some important features of Data Lake security.
5. Data Quality
Data is used to exact business value. Extracting insights from poor quality data will lead to poor quality insights.
6. Data Discovery
Data Discovery is another important stage before you can begin preparing data or analysis. In this stage, the tagging technique is used to express the data understanding, by organizing and interpreting the data ingested in the Data Lake.
7. Data Auditing
Two major Data auditing tasks are tracking changes to the key dataset.
- Tracking changes to important dataset elements.
- Captures how/ when/ and who changes to these elements.
Data auditing helps to evaluate risk and compliance.
8. Data Lineage
This component deals with the data origins. It mainly deals with where it movers over time and what happens to it. It eases errors and corrections in a data analytics process from origin to destination.
9. Data Exploration
It is the beginning stage of data analysis. All given components need to work together to play an important part in Data Lake building which can easily evolve and explore the environment.
There is always a confusing discussion between Data Lake and Data warehouse, so let us make clear the differences –
Data Lakes VS Data Warehouse
|Parameters||Data Lakes||Data Warehouse|
|Data||It stores every raw data.||It focuses only on structure or business.|
|Processing||Data are mainly unprocessed.||Highly processed data.|
|Type of Data||It can be Unstructured, semi-structured and structured.||It is structured.|
|Users||It is mostly used by Data Scientist.||Business professionals widely use Data Warehouse.|
|Storage||Data Lakes design for low-cost storage.||Expensive storage that gives fast response time are used.|
|Security||Offers lesser control.||Allows better control of the data.|
|Schema||No predefined schemas.||Predefined schemas.|
|Data Processing||Helps for fast ingestion for new data.||Time-consuming to introduce new content.|
|Data Granularity||Data at a low level of detail or granularity.||Data at the summary or aggregated level of detail.|
|Tools||Can use open source/tools like Hadoop/ Map Reduce.||Mostly commercial tools.|
Top List of Data Management Platforms:
- Google cloud platform
- Hewlett Packard Enterprise
A Short review:
- Data Lake is a storage repository which can have a large amount of raw data like structured, semi-structured and unstructured data.
- The main objective of building a Data Lake is to offer an unrefined view of data to data scientists.
- Unified operations level, Processing level, Distillation level and HDFS are important layers of Data Lake Architecture.
- Data Ingestion, Data storage, Data quality, Data Auditing, Data exploration, Data discover are some important components of Data Lake Architecture.
- All given components need to work together to play an important part in Data Lake building easily and for exploring the environment.
I have briefed about the concept of Data Lake and the difference between Data Lakes and Data Warehouse. I think you might have obtained some Knowledge on this topic.