Data Lake Vs Data Warehouse – Essential differences
Data lakes and Data warehouses are essential technologies for reporting, data analysis, and data science, but the differences may not be apparent upon a cursory reading. It is often that business users confuse a data lake to a data warehouse. They both kind of sound the same, seem like they do the same thing, but then why do we have both Data Lakes and Data Warehouses? It is a question that stumps not only business users, also many professionals who are in IT and data analytics and yet don’t quite understand the difference. In this blog, we try to unravel the mystery around Data lake vs Data warehouse and try to illustrate via simple examples and use cases, what is a data lake and a data warehouse and when to choose one over the other.
Before we jump into talking about data lakes vs data warehouses, let us look a bit deeper into the purpose of these technologies. At a high level, both data lakes and data warehouses are technologies to store and process data. To this existent, there is no difference between the two. However, when we start to understand the key attributes of the subject of the above sentence, i.e. “data” things get a bit more interesting.
Data is broadly classified into structured data, Semi-Structured data, and unstructured data. Let me give you a few examples.
|Log files data||Y||Y|
Now, let us look at the verbs in the sentence above – Storing and Processing.
Not all data is created equal. Data also loses its significance over time. So, what governs the rules for storing and processing data? – Business Requirements
It is your business requirements that determine
- When to store your data?
- Where to store your data?
- How long should you store your data?
- How much must you be willing to pay for this storage?
It is your business requirements that also determine
- How to process the data in storage?
- How quickly this data needs to be processed?
- How frequently this data needs to be processed? and
- Who/which applications consume the processed data?
Data lakes are for scenarios where the
- The volume of data is vast.
- You have both structured and semi-structured data.
- You don’t want to purge any data from your applications.
And data warehouses are for scenarios where the
- The volume of data is not massive – few GBs to a few TBs.
- You have mostly structured data.
- You want fast enterprise reporting.
What is a Data Lake?
A data lake is a consolidated repository for both your structured and unstructured data. Data Lakes have gained in prominence since the mid-2000s when mobile phones, cloud technologies, and internet-scale companies have started dominating our daily lives.
The idea of the data lake is simple –
- You have a ton of data that
- you want to store inexpensively,
- for long periods,
- use it for various needs perhaps immediately or later, and
- postpone the decision of how to structure your data until the time you must process the data
What is a Data Warehouse?
As the name suggests, a Data warehouse is a solution (warehouse) designed to store structured data. Some data warehouses also support unstructured data, but the primary use case of a data warehouse is to store and process structured data. The data volumes that are typically associated with a data warehouse are in the small to medium range. In contrast, data volumes related to a data lake are usually large to massive. Data Warehouses have been a staple for enterprise reporting and business reporting since the 1990s, and they continue to do so. The technology has evolved, and on-premises data warehouses are now ceding ground to cloud-native data warehouses. The new cloud data warehouses are truly transforming business analytics by making it easy and affordable to operate a data warehouse for a business of any size.
Data Lakes vs Data Warehouses
The table below highlights the key differences between a data lake and a data warehouse.
|Criteria||Data Lake||Data Warehouse|
|Storage||Primarily used to store unstructured data Raw data is stored in its native form and gets transformed when it is analyzed. Can also be used to store structured data A large volume of data||Primarily used to store structured data The data is cleaned and transformed before loading into the data warehouse.|
|Size||Can be up to petabytes||Generally a few Gigabytes or Terabytes|
|Data Ingest||Supports batch, real-time, and streaming data ingest||Supports batch, real-time, and streaming data ingest. But more often than not, used for batch data ingest.|
|Purpose||Ideal for Machine Learning and Deep Learning use cases like Personalized recommendations, forecasting, autonomous driving, etc.||Ideal for uses such as monthly reports, executive reporting, business analytics|
|Schema||Schema on read is the preferred approach which leads to faster data ingestion and more flexibility down the line.||Schema on write is the preferred appraoch which leads to more upfront effort, delivers more structure while being less flexible.|
|Utility||Unstructured data, explorations, innovation, flexibility.||Structured data, high performance, repeatability, constant use.|
|Users||Data Scientists, Software Engineers||Data Analysts, BI Developers, Business Users|
|Skills required||Spark, Kafka, Python, Java, Hive, etc.||SQL, Python, or R|
|Supported Use cases||Predictive analytics, Machine learning, Deep learning, NLP, etc.||Enterprise reporting – sales dashboards, marketing dashboards, web scorecards, etc.|
|Data Pipeline||ELT methodology – Extract, Load, and Transform later||ELT or ETL process depending on the use case|
|Key guiding principle||Data accuracy and completeness||Support for high data volume, variety, value, and integrity of data|
|Benefits||Highly secure and performant||Highly available and scalable|
Data lake Vs Data warehouse – What is right for you?
Most businesses that are serious about becoming data and insights-driven tend to have both. If you are a business that is in the early stages of adopting data to drive business decisions, then you may want to start off with a data warehouse. Automate repeatable enterprise reporting by leveraging a cloud data warehouse and reduce the time spent on manual assimilation of reports. For requirements that involve unstructured data analysis or dealing in large volumes of data, separate data pipelines into a data lake should be set up to get the raw data delivered in a data lake. Unleash your data scientists to find insights and drive positive business outcomes.
How can Daton help?
Data movement and data consolidation is the first and most critical stage in any data lake or a data warehousing initiative. As the diversity of your applications increase, so does the complexity of your data pipeline. Consolidating data from sales, marketing, analytics, Clickstream, customer support, log files, etc. is daunting even for seasoned data engineers. It is not only daunting, but it is also costly to build and manage in-house. Why re-invent the wheel when Daton can you consolidate all your enterprise data in just a few clicks?
Whether you are building a data lake or a data warehouse like BigQuery, Snowflake, Redshift, Daton can help you quickly assemble your data in one place. Daton is a cloud-based ETL Platform designed to simplify your data pipeline and get you working on the aspects of your project that are revenue-generating while leaving the plumbing to Daton.
Daton is a secure, fast, flexible, and cost-effective way cloud data pipeline that can accelerate your journey to insights. Try Daton for free today or reach out to us to if you’d like to discuss your project.