“Data Lake” is a massive, easily accessible data repository for storing “big data”. Unlike traditional data warehouses, which are optimized for data analysis by storing only some attributes and dropping data below the level aggregation, a data lake is designed to retain all attributes, especially when you do not yet know what the scope of data or its use.
Data Lake vs. Data Warehouse
Data warehouses are large storage locations for data that you accumulate from a wide range of sources. For decades, the foundation for business intelligence and data discovery/storage rested on data warehouses. Their specific, static structures dictate what data analysis you could perform. Data warehouses are popular with mid- and large-size businesses as a way of sharing data and content across the team- or department-siloed databases. Data warehouses help organizations become more efficient. Organizations that use data warehouses often do so to guide management decisions—all those “data-driven” decisions you always hear about.
A data lake holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data. Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags. When a business question arises, the data lake can be queried for relevant data, and that smaller set of data can then be analyzed to help answer the question.
Now that data storage and technology is cheap, information is vast and newer database technologies don’t require an agreed upon schema up front, discovery analytics is finally possible. With data lakes, companies employ data scientists who are capable of making sense of untamed data as they trek through it. They can find correlations and insights within the data as they get to know it.
Five key components of a data lake architecture:
1.Data Ingestion: A highly scalable ingestion-layer system that extracts data from various sources, such as websites, mobile apps, social media, IoT devices, and existing Data Management systems, is required. It should be flexible to run in batch, one-time, or real-time modes, and it should support all types of data along with new data sources.
2.Data Storage: A highly scalable data storage system should be able to store and process raw data and support encryption and compression while remaining cost-effective.
3.Data Security: Regardless of the type of data processed, data lakes should be highly secure from the use of multi-factor authentication, authorization, role-based access, data protection, etc.
4.Data Analytics: After data is ingested, it should be quickly and efficiently analyzed using data analytics and machine learning tools to derive valuable insights and move vetted data into a data warehouse.
5. Data Governance: The entire process of data ingestion, preparation, cataloging, integration, and query acceleration should be streamlined to produce enterprise-level Data Quality. It is also important to track the changes to key data elements for a data audit.
Like big data, the term data lake is sometimes disparaged as being simply a marketing label for a product that supports it. However, the term is being accepted as a way to describe any large data pool in which the schema and data requirements are not defined until the data is queried.
The data lake promises to speed the delivery of information and insights to the business community without the hassles imposed by IT-centric data warehousing processes.
Data Lake Advantages
- Data Lake gives business users immediate access to all data.
- Data in the lake is not limited to relational or transactional
- With a data lake, you never need to move the data
- Data Lake empowers business users and liberating them from the bonds of IT domination
- Data Lake speeds delivery by enabling business units to stand up applications quickly
- Helps fully with product ionizing & advanced analytics
- Offers cost-effective scalability and flexibility
- Offers value from unlimited data types
- Reduces long-term cost of ownership
- Allows economic storage of files
- Quickly adaptable to changes
- The main advantage of data lake is the centralization of different content sources
- Users, from various departments, may be scattered around the globe can have flexible access to the data
Data Lake Disadvantages
- Unknown area of Data Processing
- Data governance
- Dealing with Chaos
- Privacy issues
- Complexity of Legacy Data
- Metadata Lifecycle Management
- Desolate Data Islands
- The Issue of Integration
- Unstructured Data may lead to Ungoverned and Unusable Data, Disparate and Complex Tools
- Increases storage & computes costs
- There is no way to get insights from others who have worked with the data because there is no account of the lineage of findings by previous analysts
- The biggest risk of data lakes is security and access control. Some data can be placed into a lake without any oversight, as some of the data may have privacy and regulatory need
There are many organizations that are making this approach a reality, the internal infrastructures developed at Google, Amazon, and Facebook provide their developers with the advantages and agility of the data lake dream. For each of these companies, the data lake created a value chain through which new types of business value emerged:
- Using data lakes for web data increased the speed and quality of web search
- Using data lakes for clickstream data supported more effective methods of web advertising
- Using data lakes for cross-channel analysis of customer interactions and behaviors provided a more complete view of the customer
- Data lakes can give retailers profitable insights from raw data, such as log files, streaming audio and video, text files, and social media content, among other sources, to quickly identify real-time consumer behavior and convert actions into sales. Such 360-degree profile views allow stores to better interact with customers and push on-the-spot, customized offers to retain business or acquire new sales.
- Data lakes can help companies improve their R&D performance by allowing researchers to make more informed decisions regarding the wealth of highly complex data assets that feed advanced predictive and prescriptive analytics.
- Companies can use data lakes to centralize disparate data generated from a variety of sources and run analytics and ML algorithms to be the first to identify business opportunities. For instance, a biotechnology company can implement a data lake that receives manufacturing data, research data, customer support data, and public data sets and provide real-time visibility into the research process for various user communities via different user interfaces.
Regardless of where you are now, take some time to look to the future. We’re on a journey towards connecting enterprise data together. As business is increasingly becoming pure digital, access to data will become a critical priority, as will speed of development and deployment. The data lake is a dream that can match those demands. The global data lake market was valued at $7.9 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 20.6 percent by 2024 to reach $20.1 billion.
Ahmed Banafa, Author the Books:
Read more articles at: Prof. Banafa website
http://hortonworks.com/wp-content/uploads/2014/05/TeradataHortonworks_Datalake_White-Paper_20140410.pdfShare this post via: