Data Warehouse vs Data Lake?

Data lakes and data warehouses are both types of data storage systems used by organizations to manage their data. However, they differ in terms of their purpose, structure, and usage.

What are Data warehouses?

• Storage optimized for analysis: A data warehouse is a centralized repository of structured data that has been optimized for reporting and analysis. It is typically designed to support business intelligence (BI) applications and is used to store data from a variety of sources, such as transactional systems, CRM applications, and web analytics tools.
• Predefined relational database schema: Data warehouses typically use a relational database schema and ETL (extract, transform, load) processes to transform and load data from source systems into the warehouse. Data in a data warehouse is usually organized according to a predefined schema and data models, which facilitate complex querying and analysis.
• Examples: Amazon Redshift, Snowflake

What are Data lakes?

• Store raw data: A data lake is a large, centralized repository of unstructured and structured data that allows organizations to store data in its native format without prior transformation.
• Store a wide variety of data: Data lakes are designed to support a wide range of analytics use cases, including data exploration, machine learning, and real-time analytics. They can store large volumes of data from a variety of sources, including structured, semi-structured, and unstructured data.
• Examples: AWS S3, Azure Data, Apache Hadoop/HDFS

Data Warehouse vs Data Lake

• Unlike data warehouses, data lakes do not rely on predefined schemas or data models, which allows organizations to store and process data in a more flexible and agile way.
• Data warehouses are optimized for reporting and analysis of structured data, while data lakes are designed to support a wider range of analytics use cases and allow for more flexible storage and processing of both structured and unstructured data

https://www.qubole.com/data-lakes-vs-data-warehouses-the-co-existence-argument

New Term : Data Lakehouse

A data lakehouse is a new concept that combines the best of both data lakes and data warehouses. It is essentially a hybrid of a data lake and a data warehouse that enables organizations to store, manage, and analyze data in a more efficient and scalable way.

• A data lakehouse uses a unified platform to store data in its raw form, just like a data lake. However, it also includes additional features and capabilities that make it more like a data warehouse. For example, a data lakehouse may include data modeling, schema enforcement, indexing, and query optimization features, which are commonly found in data warehouses.
• Additionally, a data lakehouse can support a wide range of analytics use cases, including ad-hoc queries, machine learning, and real-time analytics, without the need for data transformation or ETL processes.

Tradeoffs: Data lakehouses are complex systems that require significant technical expertise to set up and manage. Building and maintaining a data lakehouse can be expensive, especially when compared to traditional data storage systems Data lakehouses may not perform as well as traditional data warehouses when it comes to querying and analyzing data

• Ex: Databricks Delta Lake

How does the Data Lake pipeline work?

https://www.2ndwatch.com/blog/what-is-a-data-pipeline-and-how-to-build-one/

The flow of data through a data lake can be broken down into:

1. Data ingestion: The first step in the data lake flow is to ingest data from a variety of sources. This can include structured data from databases, semi-structured data from XML or JSON files, and unstructured data from text documents, images, or videos. Ingestion can be done in batch mode or real-time, using technologies such as Apache Kafka, Flume, or Sqoop.
2. Storage: Once the data is ingested, it is stored in the data lake in its raw format. Unlike traditional databases or data warehouses, data lakes do not require data to be transformed or structured before it is stored. This allows for greater flexibility and scalability in terms of the types and volumes of data that can be stored. Data can be stored in its raw format or transformed into a more structured format using tools such as Apache Hive or Spark.
3. Processing: After data is ingested and stored in the data lake, it can be processed and transformed as needed. This may involve cleaning, filtering, and aggregating the data, as well as applying algorithms and models to extract insights and patterns. For cleaning, transforming, and enriching the data we use tools such as Apache Spark or Apache Flink, Pig, or MapReduce.
4. Data access: Finally, the data is made accessible to users for analysis using a variety of tools and technologies, such as BI tools, machine learning frameworks. Data scientists and analysts can use a wide range of tools and languages such as SQL, Python, R, or Scala to analyze the data and gain insights.
5. Visualization: Finally, the insights gained from the analysis are visualized using various tools such as Tableau, Power BI, or QlikView. This helps to communicate the insights to business stakeholders in a more intuitive and actionable way.

The data lake flow and pipeline can be automated using workflow management tools such as Apache Oozie, Apache Airflow, or Azkaban. This helps to streamline the process and make it more scalable and reliable.

When to use data warehouse?

• You need to store large amounts of data in their raw and unprocessed format. You want to store this data in its raw form to preserve its integrity and enable future analysis.
• You want to store a variety of data types, such as structured, semi-structured, and unstructured data
• You want to enable data discovery by allowing analysts to explore data without a predefined schema or structure.
• You need to support multiple use cases across the organization, such as analytics, data science, and ad hoc analysis.

When to use data lake?

• You need to store structured data that has been processed, cleaned, and transformed for analysis and reporting
• You want to provide a consistent and reliable view of the data across the organization. You want to ensure data consistency and accuracy by applying a rigorous data integration process.
• You need to support business intelligence, reporting, and data analysis
• You want to enable data governance and ensure data quality and accuracy

Challenges in Data Lake Storage:

1. Maintaining Data Quality: Data lakes are often built to store raw data, which means that data quality can be an issue. Without proper data validation and quality checks, data can be inconsistent, incomplete, or inaccurate, which can lead to incorrect analysis and decision-making.
2. Ensuring data governance: Data lakes can quickly become too large or disorganized to function efficiently. It is important to have a clear data governance strategy in place to ensure that data is managed effectively and securely, and that access is controlled and monitored. It is important to have a clear data access and usability strategy in place to ensure that data is easily discoverable and usable by all stakeholders.
3. Ensuring low storage cost: Data lakes require large amounts of storage capacity and can be expensive to manage and maintain. It is important to have a clear storage and management strategy in place to ensure that data is stored efficiently and cost-effectively.

In Next Blog

• When to use Data Lakehouse?
• How data warehouse pipeline works?
• Challenges in storing data in a data warehouse?