Data lakes and big data are two important concepts in the world of data management and analytics. Though related, they represent different approaches and architectures for storing and analyzing large volumes of data from various sources.
This article provides an overview of data lakes and big data, compares the two concepts, and provides examples of when each approach might be preferable.
What is a Data Lake?
A data lake is a centralized repository that permits you to store all your structured and unstructured data at any scale. Some key characteristics of a data lake are given here:
Massively Scalable Storage
Data lakes are built to store and analyze vast amounts of data. They can scale into the petabytes and beyond without degrading performance. Data lakes use low-cost storage on platforms like Hadoop and cloud object storage.
Multiple Data Types and Sources
A data lake can ingest structured, semi-structured, and unstructured data from a variety of sources like databases, mobile apps, social media, sensors, etc. The data is stored in native formats.
In a data lake, schema is applied to the data when it is read/analyzed instead of at the time of capturing the data (as in traditional databases). This provides flexibility to store data first now and develop schemas later.
A data lake serves as a centralized repository inclusive of data from across an organization, including line-of-business systems, applications, social media and more.
Because data lakes utilize commodity hardware and object storage in most implementations, they can store massive amounts of data very cost-effectively.
Examples of data lakes: Amazon S3, Microsoft Azure Data Lake Storage, Hortonworks Data Platform
What is Big Data?
Big data refers to extremely large and complex datasets made up of a variety of data types that traditional data warehousing and processing systems cannot easily handle. Key elements that characterize big data are:
Scale of data in terabytes, petabytes and beyond. Social media posts, server logs, and mobile data can accumulate to big data volumes very quickly.
Rate at which data accumulates. For example, IoT sensors or stock trading systems generating thousands of events per second.
Different types of structured, semi-structured and unstructured data like text, sensor data, audio, video etc. all in one system.
Requires New Tools
Traditional SQL databases cannot handle big data effectively. It requires massively parallel software running on clusters of commodity hardware.
Examples of big data systems: Apache Hadoop, NoSQL databases like Cassandra, MongoDB
Key Differences Between Data Lakes and Big Data
While the terms data lake and big data are sometimes used interchangeably, they represent different ideas in some important ways:
Data Storage and Processing
Data lakes focus more on storing vast amounts of raw data in its native format. Big data emphasizes sophisticated distributed data processing using specialized tools like MapReduce and Spark SQL.
Data Lakes allow schema-on-read, only assigning schema while reading data. Big data systems like NoSQL document databases and Hadoop require more predefined schema.
Data lakes aim for gathering all data into one repository for later exploration. Big data systems focus on real-time or batch data processing for immediate analytics needs.
Data lakes serve broad analytical needs across the organization. Big data systems are more optimized for data scientists and analysts working with statistical algorithms or machine learning.
While both can store unstructured data, data lakes can handle greater variety from more sources, especially images, video, emails and more. Big data systems are more oriented to high volume numerical and textual data.
Data lakes leverage cheap object storage like S3 and open source technology like Hadoop. Big data systems take advantage of both open source tools plus specialized distributed databases optimized for certain data types.
Data Lake VS Big Data Comparison Table
This table summarizes the key differences between data lakes and big data:
|Basis for Comparison
|Store vast amounts of raw, unprocessed data from many sources in native formats
|Enable high performance data processing workloads for analytics and machine learning
|Hadoop distributed file system (HDFS), object storage like S3
|Apache Hadoop, Spark, specialized NoSQL databases
|Schema-on-Read while analyzing the data
|Schemas predefined at data ingestion time
|Slower query performance given focus on low-cost storage and flexibility
|Very high throughput and fast parallel query processing
|Data scientists, business analysts
|Data engineers, data scientists, data analysts
|Types of Analytics
|Basic data exploration, dashboarding, ad-hoc queries
|Advanced analytics, iterative machine learning, interactive SQL
|Nearly any digital system within a company
|Events, transactions, sensors, web and mobile apps
|Supported Data Types
|All types including text, images, video, much less structured
|High volume, highly structured numeric data
|Very low-cost platform leveraging commodity infrastructure
|Can be higher given specialized compute and storage resources
When to Use Each Approach?
Reasons to Implement a Data Lake
- Need to pull together data from disparate sources across the organization for unified analytics
- Early stages of data collection when schemas and ideal data organization is still unclear
- Desire to apply machine learning and AI techniques on vast sets of heterogeneous data
- Need to store raw data for extended periods for audit purposes
Reasons to Deploy a Big Data Architecture
- Ingesting and analyzing massive amounts of streaming event data in real-time
- Running intensive data processing jobs like analytics, machine learning and graph algorithms on your data
- Storing terabytes/petabytes of structured high-velocity data that needs to be accessed and processed in parallel
- Querying data using SQL-like interfaces including Presto, Hive and Spark SQL
The approaches are complementary. Many organizations implement both data lakes and big data platforms to realize the full potential value from their data assets.
Example Combining Data Lake and Big Data
Here is a common example of how data lake and big data technologies can work together in an ideal scenario:
Stream Data to Data Lake
Continually ingest real-time data streams from online apps, IoT devices and other sources into cloud object storage like Amazon S3 or Azure Blob Storage.
Refine and Prepare Data
Pull data subsets of interest from the data lake, clean and preprocess data as needed using services like AWS Glue or Databricks.
Analyze and Train Models
Carry out batch analytics on prepared datasets or train machine learning models using Spark MLlib on platforms like EMR or Databricks.
Serve Predictions to Applications
Push model predictions to online, real-time applications to personalize user experiences. Continually retrain models as new data arrives.
This demonstrates an end-to-end pipeline leveraging the strengths of both the flexible data lake for storage and robust big data tools for processing. The platforms complement each other to enable impactful insights.
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