What is Data Warehouse

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A data warehouse consists of standardized data from multiple sources. Read more about a data warehouse.

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What is a data warehouse
A data warehouse consists of standardized data from multiple sources. The data is ingested and then transferred to the warehouse – It is ready-to-use data. The data warehouse has several applications like:
  • System Integration

    The warehouse can be bidirectional, outputting data to authorized systems. In the system, integration data is transferred from one system to a warehouse and then retrieved by a different system.

  • Consolidation

    Business functions work better when there is a single, unified data source.

  • Analytics and business Intelligence

    A data warehouse offers a standardized view of all business data. This can provide more accurate insight into the state of business than individual analytics.

  • Storage

    Warehouse stores a copy of production data in a secure location. This can be important for compliance reporting and business continuity planning.

Operating a data warehouse
It mainly consists of two elements: A data warehouse environment and a data pipeline.
Data warehouse environment
Data warehouses, though small, but can hold enormous quantities of structured data. This can be done with some on-premise systems. The most popular options for local hosting are
  • IBM
  • Microsoft Azure
  • Oracle
Such kind of storage is economically non-viable. Most warehouses are hosted on cloud service which is a cost-effective and scalable alternative. Some popular cloud storage options include
  • Snowflake
  • Amazon Redshift
  • Google BigQuery
So, it is important to have the right environment to host data. It also must be compatible with the data pipeline.
Data Pipeline
A data pipeline is a software running through three key stages: Extract, Transform, Load
  • Extract

    Data is obtained in a variety of formats. It may contain invalid or duplicate entries.

  • Transform

    Different stages of transformation include harmonization, normalization, and cleansing.

  • Load

    Data is copied from the staging database into the warehousing environment.

Data warehouse and storage
A data warehouse can only be used to store structured data. Different data types are:
  • Structured

    The information is stored in a relational database table. It includes data from most production systems, such as Enterprise Resource Planning Solution or Customer Relationship Management System.

  • Semi Structured Data

    They have a logical structure other than a relational database table. A CSV file is one example of such data-It is a text file, but formatted in a way that is easy to import into a database.

  • Unstructured Data

    Any other form of data including text documents, images, and audio files. These cannot be warehoused without being converted to structured data.

  • Metadata

    It might hold information that points to other data including unstructured data. Because metadata itself is usually structured or semi-structured.

  • Load

    Data is copied from the staging database into the warehousing environment.

Difference between data warehouse, data mart and delta lake
Different data repositories are being confused with data warehouse repositories such as delta lakes or data marts but they do differ.
  • Delta Lake

    Unlike a warehouse, delta lake can hold both structured and unstructured data. This is because it does not pass through the transformation stage before ingestion into a lake which makes the process faster.

  • Data Mart

    It is a small data warehouse. While a warehouse may store information about the whole organization, a data mart only stores information about a specific department, project, or objective.

Explore how Lyftrondata in empowering Power BI users with actionable insights.

How Lyftrondata Warehouse is built for unbeatable Security and decision-making insights

Lyftrondata Warehouse supports bulk data loading from the source of your choice. The logical data warehouse layer of Lyftrondata warehouse provides parallel access to both Lyftrondata Warehouse and all data sources in one place. The self-service data management portal empowers data-savvy users in their routine tasks.
With its agile-based infrastructure, we can build huge applications easily and ensure end-to-end meta-data management for data warehouse initiatives. It combines traditional data loading with Logical Data Warehousing to improve collaboration and simplify data management.

  • It helps to access the data instantly by creating Virtual Data Warehouse and query data in one place, in real-time, or through cached data.
  • It empowers data users to find and manage their data sets by creating a global data catalog of all valuable data sources.
  • It is a next-generation data warehouse built for performance and advanced analytics.
Lyftrondata warehouse integration architecture
With the brilliantly spun architecture, Lyftrondata Warehouse manages the complex data securely and empowers data enthusiasts to drive actionable insights in minutes.
  • Services

    The outermost layer functions to federate by streamlining data management, handling secured transactions and optimizing the whole data transformation process. It is meant to process voluminous data at high speed for unbeatable performance.

  • Security

    It is considered the heart of Lyftrondata and handles the function of enforcing security and encryption key management. This functions for encryption, tagging, masking, access rights, and role management.

  • Metadata

    Under the security layer, Lyftrondata possesses metadata that works to improve the overall performance capacity. It takes care of scheduler, alerts, workflow, data catalog, logs, monitoring, execution plans, and more.

  • Sharing

    The fourth layer functions to ensure a data set can be shared internally between different operating groups and can also be easily transported to the external commodities, eliminating the need for a sharing tool. It promises secured and governed data sharing to all users.

  • Compute

    It functions for optimized process and better load performance with push down on inbuilt Spark, AWS EMR, Azure, Snowflake, Databricks, and more. It processes voluminous data effectively and efficiently at lightning speed.

  • Storage

    The innermost layer is responsible for storing data securely and effectively. It delivers the most updated data sets and empowers users to combine them with their existing data for the deepest insights. Lyftrondata Warehouse is a future-proof solution for data management, data synchronizing, collaboration and sharing.

Write to us at [email protected] to explore how
Lyftrondata Data Warehouse is revolutionizing the concept of dealing with data and creating a niche in data warehousing.

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