Data Lake vs Data Warehouse - Working Together in the Cloud

Data Lake vs Data Warehouse - Understand the pros and cons of each of the storage options to decide which one is the best for your big data use case.

Data Lake vs Data Warehouse - Working Together in the Cloud
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“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later”

The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data.  Often they are used interchangeably but they are totally different on how the data is structured and processed. If you’re a big data engineer and finding it difficult to decide whether to use a data lake or a data warehouse for your organizational needs then we’ve got you covered.


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Data Lake vs Data Warehouse - The Differences

Before we closely analyse some of the key differences between a data lake and a data warehouse, it is important to have an in depth understanding of what a data warehouse and data lake is.

Data Lake vs Data Warehouse - The Introduction

data lake vs data warehouse Gartner

What is a Data warehouse?

According to Wikipedia, a Data Warehouse is defined as "a system used for reporting and data analysis. Data warehouses are central repositories of integrated data from one or more disparate sources. They store current and historical data in one place and are used to create analytical reports for workers throughout the enterprise."  This means that a data warehouse is a collection of technologies and components that are used to store data for some strategic use. Data is collected and stored in data warehouses from multiple sources to provide insights into business data. Data warehouses store highly transformed, structured data that is preprocessed and designed to serve a specific purpose. Data is generally not loaded into a data warehouse unless a use case has been defined for the data. Data from data warehouses is queried using SQL.

Data Warehouse Architecture 

The Data Warehouse Architecture essentially consists of the following layers:

database vs data warehouse vs data lake

Source Layer: Data warehouses collect data from multiple, heterogeneous sources. The data to be collected may be structured, unstructured or semi-structured and has to be obtained from corporate or legacy databases or maybe even from information systems external to the business but still considered relevant.

Staging Area: Once the data is collected from the external sources in the source layer, the data has to be extracted and cleaned. In data warehouses, the data is expected to be as per a specific format. In this layer, the data gets converted from its raw format into a format that abides by the schema required by the data warehouse. The ETL (extract, transform, load) tools transform, filter and validate the data. Any inconsistencies found in the data are removed, and all gaps that can be filled are filled to ensure that the data maintains integrity.

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Data Warehouse Layer: Once the data is transformed into the required format, it is saved into a central repository. The data warehouse layer consists of the relational database management system (RDBMS) that contains the cleaned data and the metadata, which is data about the data. The RDBMS can either be directly accessed from the data warehouse layer or stored in data marts designed for specific enterprise departments. Metadata contains information such as the source of data, how to access the data, users who may require the data and information about the data mart schema.

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Data Marts: Data Marts may be segregated based on enterprise departments and store information related to a specific function of an organization. Data marts contain a subset of the data in data warehouses. 

Analysis Layer: The analysis layer supports access to the integrated data to meet its business requirements. The data may be accessed to issue reports or to find any hidden patterns in the data. Data mining may be applied to data to dynamically analyze the information or simulate and analyze hypothetical business scenarios. To support this efficiently, a good analysis layer should have complex query optimizers, GUI that is easy to understand and use and information navigators.

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What is a Data lake?

As per the Wikipedia definition, a data lake is "a system or repository of data stored in its natural/raw format, usually, object blobs or files. A data lake is a single store of data including raw copies of source system data, sensor data, social data etc." A data lake is a repository that can store large amounts of structured, unstructured and semi-structured data. Data is captured into a data lake in its raw and unprocessed form, similar to how lakes have multiple tributaries flowing into them. Data lakes support data with various formats and unknown schemas like flat files, weblogs and other structures. The data captured by a data lake does not necessarily have to be of immediate use but may be stored in the data lake for future use. Since vast amounts of data is present in a data lake, it is ideal for tracking analytical performance and data integration. Data in data lakes may be accessed using SQL, Python, R, Spark or other data querying tools.

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Data Lake Architecture

data vault vs data lake vs data warehouse

Data lake architecture incorporates various search and analysis methods to help organizations glean meaningful insights from the large volumes of data. Data lakes have a flat architecture to meet a wide range of business requirements. The architecture of a data lake consists of the following layers:

Ingestion Layer: In this layer, data is loaded from various sources. The type of data being captured into a data lake can be structured, semi-structured or unstructured. It can also be loaded into the data lake in batch format or real-time streaming format. 

Storage Layer: This is a centralized repository where all the data loaded into the data lake is stored. HDFS is a cost-effective solution for the storage layer since it supports storage and querying of both structured and unstructured data. The storage layer can be considered a landing zone for all the data that is to be stored in the data lake.

Unified Operations Layer: This layer handles the governance and security of the data lake. Although a small percentage of users use the data lake, it may contain confidential data, and hence the security of the layer has to be maintained. This layer supports auditing and data management, where a close watch is kept on the data loaded into the data lake and any changes made to the data elements of the data lake. The operations layer also handles workflow management and proficiency of the data in a data lake.

Distillation Layer: When the data is required for processing, the data has to be cleaned and filtered. The distillation layer enables taking the data from the storage layer and converting it into structured data for easier analysis.

Analysis and Insights Layer: This layer supports running analytical algorithms and computations on the data in the data lake. It has to be built to support queries that can work with real-time, interactive and batch-formatted data. Insights from the system may be used to process the data in different ways. This layer should support both SQL and NoSQL queries. Even Excel sheets may be used for data analysis.

Consider the company ironSource, a leading video advertising platform that includes one of the largest in-app video networks in the industry. ironSource has to collect and store vast amounts of data from millions of devices. ironSource started making use of Upsolver as its data lake for storing raw event data. Kafka streams, consisting of 500,000 events per second, get ingested into Upsolver and stored in AWS S3. Upsolver has tools for automatically preparing the data for consumption in Athena, including compression, compaction partitioning and managing and creating tables in the AWS Glue Data Catalog. ironSource uses Upsolver to filter data and write it to Redshift to build dashboards in Tableau and send data to Athena for ad-hoc query analysis.

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Data Lake vs Data Warehouse - Storage

Data lakes are designed for low cost storage unlike data warehouses that are an expensive storage choice for large volumes of data. A Data Lake retains all formats of data regardless of its source and structure. Data is kept stored in a data lake in its raw form and only transformed when it has to be used. On the other hand, a data warehouse stores data that is cleaned and transformed after being extracted from transactional systems. A data warehouse does not generally store data that does not serve any specific purpose or data that cannot answer a particular business question. A data lake retains all data, including data currently in use, data that may be used and even data that may never actually be used, but there is some assumption that it may be of some help in the future. Data storage in data warehouses is usually more expensive and time-consuming since it has to be processed than data stored in data lakes.

In Data lakes the schema is applied by the query and they do not have a rigorous schema like data warehouses. Loading data in a data lake is easier comparatively but writing the queries is complex so retrieving data from a data lake is time consuming when compared to a data warehouse.

Data Lake vs Data Warehouse - Data Capturing

Data lakes capture raw and unprocessed data, while data warehouses capture processed data. Data in data lakes can be of all formats, including structured, unstructured and semi-structured. Data lakes capture all data irrespective of their source. Data warehouses capture structured information and store them in specific schemas that are defined for the data warehouse. 

Data Lake vs Data Warehouse - Data Timeline

Data lakes retain all data, including data that is not currently in use. Hence, data can be kept in data lakes for all times, to be usfurther analyse the data. Raw data is allowed to flow into a data lake, sometimes with no immediate use. This data gets retained for possible use in the future. When such a time comes, the data is simply accessed from the data lake. In a data warehouse, the data is generally processed. The source of the data captured is very carefully analysed and used to serve a specific purpose at a particular time.

Data Lake vs Data Warehouse - Data Processing

Data Lakes can be used as ELT (Extract, Load, Transform) tools, while Data warehouses serve as ETL (Extract, Transform, Load) tools. Data lakes and warehouses are used in OLAP (online analytical processing) systems and OLTP (online transaction processing) systems. Data lakes allow users to access raw, unprocessed data before it has been cleaned and transformed, whereas data warehouses can give users insights into specific business questions through processed data.

Data Lake vs Data Warehouse - Schema Positioning

Data warehouses follow a schema on write strategy for data processing unlike data lakes that follow a schema on read strategy.Data Lakes have a "Schema-on-Read" structure, which means that the schema in data lakes is defined after the data is stored. This makes data capture easy because data can be taken from a source without considering the nature of the data. It also offers high flexibility with the method of data manipulation when the data is required for further processing but requires more work at the time of data processing. In a data warehouse, the schema structure is "Schema-on-Write", which means that the schema is typically defined before the data gets stored. As a result, there is more work while the data is captured and stored in the data warehouse, but there is more performance and security when further analysis is required.

Data Lake vs Data Warehouse - Tasks

Data lakes contain a collection of data used and data that may be used in the future. The variety of data in a data lake makes it very useful for data analytics to be performed on large volumes of data for users who want to gain some fresh insight into the data. It allows users access to data before it is transformed and cleansed. Data warehouses primarily contain data that can provide insights to some predefined business questions and are mainly used to generate specific reports for operational users.

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Data Lake vs Data Warehouse- Users

The Data lake is ideal for users who require the data for deep analysis. Since the data will be of large volume and may consist of structured, unstructured and semi-structured data, it is ideally suited for users who possess advanced analytical tools for data analysis, including data engineers, data scientists and data analytics engineers. They can use their big data tools to work on large and varied data sets to perform any required analysis and processing. The data warehouse consists of data that is transformed and cleaned making it best suited for operational users as it is easy to use and understand the data. Data warehouses are built to answer business-specific questions and have information on data such as key performance indicators.

Data Lake vs Data Warehouse- Advantages

Data lakes store raw, unprocessed data without taking into consideration the source of the data. This allows easy data storage since data can just be taken from a source and stored onto data lakes for a long time. Since data lakes store data that is not currently in use but may be needed at a later point in time, they are an excellent source for data analysis. In addition, data lakes are very adaptable to any change in the inflowing data since there is no predefined schema for the data getting stored in a data lake.

Since data warehouses store cleaned and transformed data, it is beneficial for operational users who do not have to deep-dive into the data and only require the data for their use. The data is a format that is easy to understand. Most of the users in an organisation are operational users and only require reports and key performance metrics. Even in cases where further processing is required of the data since it is already cleaned, it is easier to work with it.

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Data Lake vs Data Warehouse- Disadvantages

In data lakes, since the data is kept in its raw form, it has to be transformed when ready to be used. Since large amounts of data are consistently maintained in a data lake, there may be redundant and irrelevant data. It becomes taxing to sort through the data when certain information is required. 

In data warehouses, a major issue is the difficulty faced when one tries to make changes to the data or any change in the data flowing to the data warehouse. Data warehouses usually have a predefined schema which the data has to abide by. Changes in the data may require schema modifications, which can result in rework and restructuring.

Data Warehouse vs Data Lake -  A Conclusive Table

Data Lake

Data Warehouse

Data lakes retain all data irrespective of the source and structure.

The source of data stored in a data warehouse is carefully analysed before the data is stored in the data warehouse.

Data Lakes store structured, unstructured and semi-structured data.

Data Warehouses store only structured data in an RDBMS, where the data can be queried using SQL.

The data in data lakes has to be cleaned and transformed before it is used for analysis.

Since the data in data warehouses is already cleaned and transformed, it can directly be used for further processing.

Data lakes follow Schema-on-Read.

Data Warehouses follow Schema-on-Write.

Data in data lakes may be currently in use or stored in the data lake for some possible future use.

Data does not get loaded onto a data warehouse unless it is required for a specific purpose.

Professionals who have to perform in-depth analysis and have the analytical tools are the ones who use the data in a data lake.

Data warehouses are mainly for operational users who require some reports or key performance indicators from the data.

The vast amount of data in a data lake is of good use for any data analysis that has to be performed.

Since the data is already structured in a data warehouse, it is easy to use and understand for the operational users.

Data lakes adapt to change with ease since there is no predefined schema that the data has to abide by.

Changes in a data warehouse may require rework and also be very time-consuming.

Data Warehouse vs Data Lake - The Future of Big Data

The answers to this question is it depends on the business use case in action. For instance, if you work for a social media company then the data is usually going to be(unstructured) in the form of visuals and documents with minimal structured data so data lake can be a good choice. However, if you work for an ecommerce company these companies have multiple departments generating data and data warehouses can be a good choice to get a summary of all that data. 

Often when building data pipelines, you will need to use both the storage options for optimal results. The best approach always is to have a combination of the two and it is not always about a debate between data lake vs data warehouse. You can use the data lake(economical choice) for exploratory data analysis and use the data warehouse for reporting effectively and efficiently. 

If you want to get hands-on experience understanding their differences or want to learn how to use a data lake or a data warehouse in your next project. Explore solved end-to-end Big Data projects with reusable code, guided videos, downloadable datasets, and documentation to help you through your learning journey.

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FAQs on Data Warehouse vs Data Lake

Does a data lake replace a data warehouse?

A Data lake cannot be a direct replacement for a data warehouse. While the two are both used to store large amounts of data, the format of data involved and the purpose of storing the data differ in the two cases, and hence, the two tools are built to fulfil different outcomes.

Is Snowflake a data lake or data warehouse?

Snowflake is your data lake and a data warehouse because it offers unlimited storage capaicty at economical pricing, convenience, and cloud scaling needed for a data lake and also provides security, governance, control, and performance like a data warehouse. It is also possible to use Snowflake on data stored in cloud storage from Amazon S3 or Azure Data lake for data analytics and transformation.

Is Hadoop a data lake or data warehouse?

Hadoop is a technology that can be used for building both data lakes and data warehouses. It offers tools that can support the architecture of a data lake, such as HDFS (Hadoop Distributed File System) and tools that can support the architecture of a data warehouse, such as Hive.

 

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