Data Lake > warehouse ?
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Data Warehouse:
- Data warehouses are optimized for analytical processing and support complex querying and reporting. They store structured data in a way that facilitates efficient querying and analysis.
- Data is typically cleaned, transformed, and integrated from various sources before being loaded into the data warehouse.
- Data warehouses often use techniques like star or snowflake schemas to organize data for easier retrieval and analysis.
- Popular data warehousing solutions include Amazon Redshift, Google BigQuery, and Snowflake.
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Data Mart:
- Data marts are subsets of data warehouses that are designed to serve specific business units or departments within an organization.
- They focus on providing data that is relevant to a particular business function, such as sales, marketing, or finance.
- Data marts help improve data accessibility and reduce the complexity of querying large data warehouses.
- Organizations create data marts to enable quicker access to specific insights tailored to the needs of different teams.
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Data Lake:
- Data lakes store both structured and unstructured data in its raw form, without enforcing a specific structure or schema.
- They are highly scalable and capable of storing massive amounts of data, making them suitable for handling big data and diverse data types.
- Data lakes support data exploration, experimentation, and advanced analytics, as the data can be transformed and processed as needed.
- Tools like Apache Hadoop, Apache Spark, and cloud-based solutions like Amazon S3 and Azure Data Lake Storage are commonly used for building data lakes.
As we mentioned, the decision to use one or more of these solutions depends on various factors such as the organization’s data strategy, technical requirements, budget constraints, and future scalability needs. In many cases, organizations adopt a hybrid approach, utilizing a combination of data warehouses, data marts, and data lakes to meet different data storage and analysis needs across the organization.
Why cloud data management will become the next step in your cloud journey? Source:yellowbrick
%
Of all corporate data is stored in the cloud
Value size of the cloud from 2019-2028 with an estimated value of 51.8 Billion u$d.
%
of IT managers and executives stated that they are investing more in their analytics platforms as one of their priorities in 2021
The Power of Data Analytics
Ready for your first Data Warehouse?
Data Warehouse as a Service (DWaaS)
Cybear-Tech’s rents out a full-scale data warehouse integrated with your existing BI and analytics infrastructure on a subscription fee basis.
Data Warehouse consulting services
Cybear-Tech’s team designs a data warehouse and plans out its implementation as well as renders advisory support while migrating or upgrading your legacy solution to optimize DWH performance and costs.
Data Warehouse Implementation
Cybear-Tech’s team builds a DWH tailored to your unique data consolidation and storage needs and implements it into your ecosystem.
Data Warehouse migration
Cybear-Tech helps you optimize DWH performance and lower total cost of ownership by moving your existing on-premises data warehouse to the cloud with no business process disruptions.
Data Warehouse testing
Cybear-Tech offers a comprehensive DWH testing set, which can include ETL/ELT testing, BI testing, DWH performance testing and security testing.
Data Warehouse support
Cybear-Tech provides DWH support to help you identify and solve DWH performance issues, achieve DWH stability for timely and quality data flow for business users, lower DWH storage and processing costs.
What you gainÂ
Business intelligence
Develop new strategies in record time
cost efficiency & mastery
