Written by Imran Abdul Rauf
Technical Content WriterPoor data quality costs the US economy a loss of $3.1 trillion each year, and the big data industry is reaching a worth of $103 billion by 2023. How does the rising demand affect the two most popular options for storing big data? Data lake and data warehouse.
This blog will discuss the fundamental concepts of data lake, data warehouse, the significant differences, and the considerations to note before selecting the right one for your business.
Data lakes are data repositories from disparate sources that store vast volumes of data in their original format. One prominent feature is their tendency to store data in different formats like CVS, JSON, BSON, Avro, TSV, ORC, and Parquet. A data lake is mostly used to evaluate the data and acquire valuable insights. Moreover, businesses also use data lakes as an option for cheap storage and use them for future jobs.
A data warehouse stores highly structured data or past and current information gathered from various systems. The objective of data warehouses is to merge disparate data sources for data analysis and create business intelligence in the form of dashboards and reports.
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Here are the significant differences between data lake and data warehouse through different parameters, including data type, workloads, size, users, data freshness, schema flexibility, and benefits and limitations.
Data Lake | Data Warehouse | |
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Workloads | Analytical | Analytical |
Data Type | Structured, semi-structured, and/or unstructured data | Structured and/or semi-structured data |
Size | Stored in petabytes, or 1,000 terabytes (data lakes consume immense volumes because they keep all the relevant data of the organization) | More selective in nature, depending on what kind of data is stored |
Schema Flexibility | Schema definition isn’t required for ingestion | Pre-defined and fixed schema definition is required for ingestion |
Users | App developers, business analysts, and data science professionals | Business analysts and data scientists |
Data Freshness | Updated status isn’t confirmed based on the frequency of ETL processes | Updated status isn’t confirmed based on the frequency of ETL processes |
Pros |
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The fixed schema feature helps business analysts with the available data |
Cons | Requires management and preparation of data for use |
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When companies want to analyze their data from different sources, they may choose a data warehouse, data lake, or both. Consider the following questions when determining which option is the right fit for your business.
Data lakes are usually preferred over data warehouses, but the latter is on course to make a comeback for the following reasons.
Data scientists spend around 80% of their time preparing data when developing ML models. Data warehouses have built-in transformation features which allow data scientists to easily prepare and use the data at scale. Moreover, warehouses can also reuse the functions for different analytics; in other words, you can overlay a schema across multiple features. The benefit reduces the duplication chances and improves the raw data quality.
As organizations worldwide use machine learning and data science in different industries, including telecom, fintech, insurance, etc., data warehouses will become valuable assets in the entire data ecosystem.
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