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data lake vs data warehouse
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Data Lake vs Data Warehouse: What's The Key Difference?

data lake vs data warehouse

With the rise of big data and the explosion of new data sources, traditional data warehousing approaches may not be sufficient to meet the needs of modern data management and analytics, creating confusions between Data Lake vs Data warehouse. This has led to the development of new approaches, including Data Lake and Data Warehouse. Each approach offers unique benefits and drawbacks, and understanding the differences between them is critical to making informed decisions about data management and analytics.

Data Lake

A Data Lake is a centralized repository that allows businesses to store vast amounts of raw, unstructured, or structured data at scale. It provides a flexible storage environment, enabling organizations to ingest diverse data types without the need for upfront structuring. This unrefined data can then be processed and analyzed for valuable insights, making Data Lakes ideal for handling large volumes of real-time and varied data.

Benefits and Use Cases of Data Lake

Data lakes provide scalable and cost-effective storage, accommodating diverse data types such as raw and unstructured data for flexible analysis. With a focus on real-time analytics and advanced capabilities like machine learning, they support innovation in algorithm development. Cost-efficient storage solutions, often leveraging scalable cloud storage, make data lakes economical for managing large datasets.

 

Use cases range from big data analytics, IoT data management, and ad hoc analysis to long-term data archiving and achieving a 360-degree customer view. In essence, data lakes offer dynamic repositories that empower organizations with flexibility, real-time insights, and comprehensive data management solutions.

Data Warehouse

On the other hand, a Data Warehouse is a structured, organized database optimized for analysis and reporting. It is designed to store structured data from various sources in a format that is easily query able and supports business intelligence reporting. Data Warehouses are characterized by their schema-on-write approach, requiring data to be structured before entering the system, ensuring a high level of consistency for analytical purposes.

Benefits and Use Cases of Data Warehouse

Data warehouses offer a multitude of benefits, including optimized structured data analysis for improved query performance and efficient reporting. They preserve historical data for time-series analysis and audit trails, enhance business intelligence through data consolidation and dashboard creation, ensure data quality and consistency through cleansing processes, and provide scalability to handle growing data volumes.

 

Common use cases encompass business performance analysis, customer relationship management, supply chain optimization, financial reporting and compliance, and human resources analytics.

Find the visual representation and difference between: Data Lake vs Data Warehouse.

Data Lake vs Datawarehouse: Key Differences

Features 

Data Lake

Data Warehouse 

Purpose 

 

Used for storing vast amounts of diverse data types for future analysis. 

Optimized for large-scale analytical queries, storing historical data for reporting and analysis. 

Data Type 

 

Stores raw, unprocessed data in its native format. 

Stores summarized, aggregated, and historical data. 

Data Structure 

Schema-on-read, allowing for flexibility in data storage. 

Optimized for read-heavy operations (OLAP – Online Analytical Processing). 

Users 

 

Primarily used by data engineers, data scientists, and machine learning teams. 

Mainly used by business analysts, data scientists, and decision-makers for insights and reporting. 

Data Volume 

Holds vast amounts of unstructured and structured data. 

Handles large volumes of historical data from various sources. 

Performance 

 

Performance can vary; optimized for large data ingestion rather than query speed. 

High performance for complex queries and large-scale data retrieval for analysis. 

Schema Design 

Uses a flexible schema design; data is often stored without a predefined schema. 

Denormalized schema (e.g., star or snowflake schema) for faster query performance 

Data Processing 

 

Processes a wide variety of data types, including structured, semi-structured, and unstructured data. 

Processes complex  queries requiring significant data aggregation 

Concurrency 

Supports high concurrency for data ingestion and retrieval.

 

Supports a lower number of users. 

Storage Cost 

 

Typically cheaper to store vast amounts of data due to lower storage costs. 

 

Higher storage costs due to large datasets and complex processing requirements. 

 

Example Use Cases 

 

Data exploration, machine learning, real-time analytics. 

Business intelligence reporting, trend analysis, forecasting, decision support. 

Data Source 

Captures data from various sources, including social media, IoT devices, and unstructured data. 

Aggregates data from multiple sources, including databases, external systems, and log files. 
  1. Data Type:
    Data Lake: Stores raw, unprocessed data in its native format.
    Data Warehouse: Stores summarized, aggregated, and historical data.
     
  2. Purpose:
    Data Lake: Used for storing vast amounts of diverse data types for future analysis.
    Data Warehouse: Optimized for large-scale analytical queries and historical data analysis.

  3. Data Structure: 
    Data Lake: Schema-on-read, allowing for flexibility in data storage.
    Data Warehouse: Optimized for read-heavy operations (OLAP – Online Analytical Processing).

  4. Users:
    Data Lake: Primarily used by data engineers, data scientists, and machine learning teams.
    Data Warehouse: Mainly used by business analysts, data scientists, and decision-makers.

  5. Data Volume:
    Data Lake: Holds vast amounts of unstructured and structured data. 
    Data Warehouse: Handles large volumes of historical data from multiple sources.

  6. Performance: 
    Data Lake    : Performance can vary; optimized for large data ingestion rather than query speed. 
    Data Warehouse: High performance for complex queries and large-scale data retrieval.

  7. Schema Design:
    Data Lake: Uses a flexible schema design; data is often stored without a predefined schema. 
    Data Warehouse: Denormalized schema (e.g., star or snowflake schema) for faster query performance.

  8. Data Processing:
    Data Lake: Processes a wide variety of data types, including structured, semi-structured, and unstructured data. 
    Data Warehouse: Processes complex queries requiring significant data aggregation.

  9. Concurrency:
    Data Lake: Supports high concurrency for data ingestion and retrieval. 
    Data Warehouse: Supports a lower number of users.

  10. Storage Cost:
    Data Lake: Typically cheaper to store vast amounts of data due to lower storage costs.
    Data Warehouse: Higher storage costs due to large datasets and complex processing.

  11. Data Source:
    Data Lake: Captures data from various sources, including social media, IoT devices, and unstructured data. 
    Data Warehouse: Aggregates data from multiple sources, including databases, external systems, and log files.

  12. Example Use Cases: 
    Data Lake: Data exploration, machine learning, real-time analytics. 
    Data Warehouse: Business intelligence reporting, trend analysis, forecasting. 

Finding the Right Fit: data lake vs data warehouse

Is there room for both Data Lake and Data Warehouse in your data strategy? Explore the benefits of adopting a hybrid approach, seamlessly integrating the strengths of both solutions for comprehensive data management. Discover the factors to consider when choosing between Data Lake and Data Warehouse solutions. From cost considerations to scalability needs and varying data types and formats, find the perfect fit with Global Data 365 for your business’s unique requirements by contacting us now.

Simplify Your Data – Get a Free Consultation!

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Automate Reporting from Dynamics GP
Blogs

How To Automate Reporting From Dynamics GP?

Automate Reporting from Dynamics GP

If you do not fully understand the complex underlying data structure of over 1300+ tables and 22500+ fields, then  automate reporting from Dynamics GP can prove to be a problem. Recently, Microsoft Dynamics ERP software has evolved to Dynamics 365 Business Central cloud, and it brings versatility and easy access for small businesses throughout the world. Reporting challenges are bound to arise due to the complex nature of the data structure field. All ERP solutions are made of back-ends that are hard to operate for anyone who is not a specialized expert or administrator.

ERP solutions, like Dynamics GP, are designed to optimize business processes and data storage. Its main focus is on storing data, not extracting it. Microsoft Dynamics GP has advanced and evolved in the ways users receive their reports. Management Reporter has advanced to the new setting providing an Excel-integrated tool known as Jet Basics; Smart List has for a long time provided the export-to-spreadsheet option, and Power BI is coming out as a great tool for data visualization. Similar to other Microsoft Dynamics solutions, Dynamics GP is evolving when it comes to providing built-in and extra tools for users to get data visualization.

According to the feedback received from thousands of users from across the globe, users complain about the reporting and analytics in Dynamics GP, as it fails to meet functional reporting requirements outside of financial reporting. The reason behind this could be the complex GP data structure or the unavailability of specialized experts. In any way, depending on these tools is costing users their time and money. Companies are forced to recruit Dynamics GP experts because of the lack of access to one organized, instant visualization of the data.

If you face limitations in your financial systems while regularly using Dynamics GP to optimize processes, then the main challenges that you may be facing in operational reporting are:

Challenges in reporting from Dynamics GP

The four main challenges faced by businesses in reporting from Dynamics GP are:

– SSRS Programming:
Programming in SSRS is costly and slow due to a large amount of data, intricate linking tools, and the programming skill needed to form a report.

– Managing Unorganized Data in Excel:
In a company, every person has their own spreadsheets, which can result in unreliable data and security risks. As every department has different approach in completing its operations.

– Power BI Views:
Power BI can be difficult to navigate for someone who is not a technical expert due to the compilation of data that results in a single view and the rewriting needed to change the views.

– Constructing OLAP Data Cubes:
Constructing OLAP data cubes can be hard and time-consuming as it requires a specialized expert who is familiar with both SSAS data cubes and Dynamics GP.

Keeping these challenges in mind, Global Data 365 has a solution to overcome your Dynamics GP users’ time, money, and effort, all the while creating better reports. It begins with Jet Analytics, the fundamental Dynamics GP data solution.

How Global Data 365 helps with Reporting in Dynamics GP?

Global Data 365 provides better services and implementation to address the obstacles using dynamics that lead your business to success. Without the need for developers and costly experts, Jet Analytics is a complete business intelligence solution designed to obtain quick, customizable reports and dashboards in Excel. And it is possible to do it yourself. Jet Analytics puts all the Dynamics GP data into one and organized it in one location using a pre-built data warehouse, OLAP data cubes, and dashboards to automate reporting in Dynamics GP.

If it is paired with a user-based front-end reporting tool, it makes it possible for users to view, assemble, and prepare the data, so you can benefit from the robust data visualization tools of Power BI and be more effective with operational reporting and analysis. The Jet Data Manager is used as a back-end tool by Jet Analytics to configure turnkey data warehouses and OLAP cubes to assist you monitor and organize your Dynamics GP data.

 

Jet Analytics provides users to use Excel for all your reporting requirements with one operating location for your reporting, but it offers one source and data management system to handle the delivery, security, business measures, calculations, and run-time of such reports. So, do not let your existing obstacles in operational reporting keep you from receiving immediate insight into your data. Companies can now improve the visibility of real-time data to help automate reporting from Dynamics GP users turn efficient and enjoy Jet Analytics full capabilities.

 

We, at Global Data 365, offers free one month license of Jet Analytics for you to test it with your Dynamics GP database and see the value it brings into your reporting process. Contact Us to order your trial license now.

Get 30 days free upgrade to Jet Reports.

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