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Difference between Database vs Data Warehouse vs Data Lake
What is Database?
A database is a collection of data or information. Databases are typically accessed
electronically and are used to support Online Transaction Processing
(OLTP). Database Management Systems (DBMS) store data in the database and
enable users and applications to interact with the data. The term “database” is
commonly used to reference both the database itself as well as the DBMS.
Feature of databases
•Security features to ensure the data can only be accessed by authorized users.
•ACID (Atomicity, Consistency, Isolation, Durability) transactions to ensure data
integrity.
•Query languages and APIs to easily interact with the data in the database.
•Indexes to optimize query performance.
•Full-text search.
•Optimizations for mobile devices.
•Flexible deployment topologies to isolate workloads (e.g., analytics workloads) to a
specific set of resources.
•On-premises, private cloud, public cloud, hybrid cloud, and/or multi-cloud hosting
options.
Types of Databases ?
•Relational databases: Oracle, MySQL, Microsoft SQL Server, and PostgreSQL
•Document databases: MongoDB and CouchDB
•Key-value databases: Redis and DynamoDB
•Wide-column stores: Cassandra and HBase
•Graph databases: Neo4j and Amazon Neptune
Why we need Databases ?
If your application needs to store data (and nearly every interactive application does),
your application needs a database. Applications across industries and use cases are
built on databases. Many types of data can be stored in databases, including:
•Patient medical records
•Items in an online store
•Financial records
•Articles and blog entries
•Sports scores and statistics
•Online gaming information
•Student grades and scores
•IoT device readings
•Mobile application information
What is Datawarehouse?
Data warehouses are large storage locations for data that you accumulate
sources. For decades, the foundation for business intelligence and data
data warehouses. Their specific, static structures dictate what data analysis
Data warehouses are popular with mid- and large-size businesses as a way
across the team- or department-siloed databases. Data warehouses help
efficient. Organizations that use data warehouses often do so to guide
those “data-driven” decisions you always hear about.
•Snowflake
•Yellowbrick
•Teradata
What is data lake?
A data lake is a large storage repository that holds a huge amount of raw data in its
original format until you need it. Data lakes exploit the biggest limitation of data
warehouses: their ability to be more flexible.
As we’ll see below, the use cases for data lakes are generally limited to data science
research and testing—so the primary users of data lakes are data scientists and
engineers. For a company that actually builds data warehouses, for instance, the data
lake is a place to dump and temporarily store all the data until the data warehouse is up
and running. Small and medium sized organizations likely have little to no reason to use a
data lake.
•Hadoop
•Azure
•Amazon S3
Difference between Database vs Data Warehouse vs Data Lake
Database vs Datawarehouse vs Data Lake
Database and data warehouses can only store data that has been structured. A data lake, on the other
hand, does not respect data like a data warehouse and a database. It stores all types of data:
structured, semi-structured, or unstructured.
Storing a data warehouse can be costly, especially if the volume of data is large. A data lake, on the
other hand, is designed for low-cost storage. A database has flexible storage costs which can either be
high or low depending on the needs.
Database vs Datawarehouse vs Data Lake
Data warehouse technologies, unlike big data technologies, have been around and in use for decades.
Data warehouses are much more mature and secure than data lakes.
Big data technologies, which incorporate data lakes, are relatively new. Because of this, the ability to
secure data in a data lake is immature. Surprisingly, databases are often less secure than warehouses.
That’s likely due to how databases developed for small sets of data—not the big data use cases we see
today.
Database vs Datawarehouse vs Data Lake
Data warehouses, data lakes, and databases are suited for different users:
•Databases are very flexible and thus suited for any user.
•Data warehouses are used mostly in the business industry by business professionals.
•Data lakes are mostly used in scientific fields by data scientists.
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Difference between Database vs Data Warehouse vs Data Lake

  • 2. What is Database? A database is a collection of data or information. Databases are typically accessed electronically and are used to support Online Transaction Processing (OLTP). Database Management Systems (DBMS) store data in the database and enable users and applications to interact with the data. The term “database” is commonly used to reference both the database itself as well as the DBMS.
  • 3. Feature of databases •Security features to ensure the data can only be accessed by authorized users. •ACID (Atomicity, Consistency, Isolation, Durability) transactions to ensure data integrity. •Query languages and APIs to easily interact with the data in the database. •Indexes to optimize query performance. •Full-text search. •Optimizations for mobile devices. •Flexible deployment topologies to isolate workloads (e.g., analytics workloads) to a specific set of resources. •On-premises, private cloud, public cloud, hybrid cloud, and/or multi-cloud hosting options.
  • 4. Types of Databases ? •Relational databases: Oracle, MySQL, Microsoft SQL Server, and PostgreSQL •Document databases: MongoDB and CouchDB •Key-value databases: Redis and DynamoDB •Wide-column stores: Cassandra and HBase •Graph databases: Neo4j and Amazon Neptune
  • 5. Why we need Databases ? If your application needs to store data (and nearly every interactive application does), your application needs a database. Applications across industries and use cases are built on databases. Many types of data can be stored in databases, including: •Patient medical records •Items in an online store •Financial records •Articles and blog entries •Sports scores and statistics •Online gaming information •Student grades and scores •IoT device readings •Mobile application information
  • 6. What is Datawarehouse? Data warehouses are large storage locations for data that you accumulate sources. For decades, the foundation for business intelligence and data data warehouses. Their specific, static structures dictate what data analysis Data warehouses are popular with mid- and large-size businesses as a way across the team- or department-siloed databases. Data warehouses help efficient. Organizations that use data warehouses often do so to guide those “data-driven” decisions you always hear about. •Snowflake •Yellowbrick •Teradata
  • 7. What is data lake? A data lake is a large storage repository that holds a huge amount of raw data in its original format until you need it. Data lakes exploit the biggest limitation of data warehouses: their ability to be more flexible. As we’ll see below, the use cases for data lakes are generally limited to data science research and testing—so the primary users of data lakes are data scientists and engineers. For a company that actually builds data warehouses, for instance, the data lake is a place to dump and temporarily store all the data until the data warehouse is up and running. Small and medium sized organizations likely have little to no reason to use a data lake. •Hadoop •Azure •Amazon S3
  • 9. Database vs Datawarehouse vs Data Lake Database and data warehouses can only store data that has been structured. A data lake, on the other hand, does not respect data like a data warehouse and a database. It stores all types of data: structured, semi-structured, or unstructured. Storing a data warehouse can be costly, especially if the volume of data is large. A data lake, on the other hand, is designed for low-cost storage. A database has flexible storage costs which can either be high or low depending on the needs.
  • 10. Database vs Datawarehouse vs Data Lake Data warehouse technologies, unlike big data technologies, have been around and in use for decades. Data warehouses are much more mature and secure than data lakes. Big data technologies, which incorporate data lakes, are relatively new. Because of this, the ability to secure data in a data lake is immature. Surprisingly, databases are often less secure than warehouses. That’s likely due to how databases developed for small sets of data—not the big data use cases we see today.
  • 11. Database vs Datawarehouse vs Data Lake Data warehouses, data lakes, and databases are suited for different users: •Databases are very flexible and thus suited for any user. •Data warehouses are used mostly in the business industry by business professionals. •Data lakes are mostly used in scientific fields by data scientists.
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