Category Archives: Data Warehouse / Data Management

15 Reasons Why You Should Opt for Cloud Data Warehouse?-CloudMoyo

15 reasons why you should opt for cloud data warehouse?

Data holds the utmost power to transform the business landscape. It helps discover business insights and helps decision-making. An enterprise system generates a huge chunk of data, which is certainly not a piece of cake to manage. New type of data comes with high volume, variety and velocity, popularly known as big data. Hence, the technology of modern or cloud data warehouse has the ability to transform businesses with its analytical approach.

Also Read: The future of Cloud Data Warehouse

Read on 15 reasons why you should choose a cloud data warehousing system over a traditional data warehouse technology:

  1. A cloud-based data warehousing system helps in incorporating data sources in data analysis by launching the project with faster approach
  2. A cloud data warehouse places high tag on security of the business data
  3. It is highly economical. With a cloud data warehouse, you get to pay for what you use and can vary the desired configuration and performance levels
  4. The procurement and deployment cycles are relatively quicker than that of the on-premise data warehousing system
  5. Cloud Data Warehouse makes data available at every step of modification-supporting data exploration, business intelligence and reporting
  6. Allows enterprises to shift their focus from systems management to actual analysis of data.
  7. Operates painlessly at any scale and makes it possible to combine diverse data, both structured and semi-structured
  8. Data insights can be always up to date and directly accessible to everyone who needs them
  9. There is no limitation on the number of users. A cloud data warehouse allows users of any number to use same amount of data with query performance degradation
  10. It removes the dependency on IT and democratizes access to enterprise data
  11. It can be used by individual departments like marketing, finance, development, and sales at organizations of all types and size
  12. It serves next generation requisites for an ideal data warehouse by centralizing different types of data sources into single point storage in real time
  13. Almost negligible time is spent tuning and re-architecting queries to address performance deficiencies
  14. A cloud data warehouse boasts of ultimate features like indexing and cataloging. It is designed in a way that data can be indexed, cataloged and tagged with metadata in real-time
  15. A Cloud data warehouse can also track who has used a particular data, in which format it is extracted and how has the user used it.

Still confused? Need more information on what cloud data warehouse is all about? Read our white paper to learn more about Microsoft Azure Data Warehousing system, how it benefits the business users and how it helps in transforming the business landscape.

Difference Between Data Warehouse & Data Lake|CloudMoyo

Difference between a Data Warehouse and a Data Lake

Is a data lake going to replace the data warehousing system in near future? Whether to use a data warehouse or a data lake or both? These are some of the common queries raised by the business users. Businesses should understand the concept of both data lake and data warehouse, most importantly when and how to implement them.

A data Lake is a repository that stores mountains of raw data. It remains in its native format and transformed only when needed. It stores all types of data irrespective of the fact that whether they are structured, semi-structured or unstructured.

On the other hand, a data warehouse is a storage repository that stores data that are extracted, transformed and loaded into the files and folders. A data warehouse only stores structured data from one or more disparate sources that are processed later for the business users. Data extracted from a data warehouse helps the users to make business decisions.

Read and know-Towards which direction is the Data Warehouse is moving?

What is Right for Your Company- A Data Lake Or A Data Warehouse Or Both?

Organizations, nowadays, generate a huge amount of data and access the huge number of disparate datasets. It makes the gathering, storing and analyzing of data more complicated. Therefore, these are the factors to choose data management solutions- for data gathering and storing and later analyzing them for competitive advantages. Here’s where data lakes and data warehouses help the business users in their own way. Data Lakes can be used to store a massive amount of structured and unstructured data that comes with high agility -can be configured and reconfigured when needed. The data warehouse system as a central repository helps the business users to generate one source of truth. It needs IT help whenever you use the data warehouse to set up new queries or data reports. Some data, which is incapable of providing answers to any particular query/request, is removed in the development phase of a data warehouse for optimization.
Take a deep dive into the Microsoft Azure Data Lake and Data Analytics
Classifications give Clarifications

Let’s explore and classify a few points to present some key differences between the Data Lake and Data warehouse:

  1. Data: Data Lakes embrace and retain all types of data, regardless of whether they are texts, images, sensor data, relevant or irrelevant, structured or unstructured, etc… Unlike a data lake, data warehouses are quite picky and only store structured, processed data. When the data warehouse is in its development stage, decisions are made on the grounds of which business processes are important and which data sources are to be used. A data Lake allows business users to experiment with different types of data transformations and data model before a data warehouse gets equipped with the new schema.
  2. User: Data lakes are useful for those users who are looking for data to access the report and quickly analyzing it for developing actionable insights. It allows users like data scientists who do an in-depth analysis of data by mashing up different types of data, extracted from different sources- to generate new answers to the queries. A data warehouse, on the contrary, supports only a few business professionals who can use it as a source and then access the source system for data analysis. A Data warehouse is appropriate for predefined business needs.
  3. Storage: Cost is another key consideration when it comes to storage of data. Storing data in a data lake is comparatively cheaper than in a data warehouse. A data warehouse deals with data of high volume and variety, thus, is designed for a high cost storage.
  4. Agility: A data warehouse is highly structured, therefore, comes with low agility. The data lakes, on the other hand, requires to technically change the data structure from time to time as it lack a defined structure that help developers and data scientists to easily configure queries and data model when need arises.

Below is a handy table that summarizes the difference between a Data Warehouse & a Data Lake –

Basis of Differences Data Warehouse Data Lake
Types of data Stores data in the files & folders Stores raw data (Structured/Unstructured/Semi-Structured) in its native format.
Data Retention Do not retain data Retains all the data
Data Absorption Stores transaction system or quantitative metrics Stores data irrespective of volume and variety
User Non-cosmopolitan like the business professionals Cosmopolitan-the Data scientists
Processing Schema-on-write, meaning- cleansed data, structured Schema-on-Read, raw data which only transforms when needed
Agility Needs fixed configuration-less agile Configuration and reconfiguration are done when required-Highly agile
Reporting and Analysis Slow and expensive Low storage, economical

In the concluding lines, it is quite tempting to say, “go with your current requirements” but let me advocate you here that if you have an operative data warehouse just go for implementing a data lake for your enterprise. Alongside, your data warehouse, the data lake will operate using new data sources you may want to fill it up with. You can also use the data lake as an archive storage and like never before, let your business users access the stored data. Finally, when your data warehouse starts to age you can either continue it by using the hybrid approach or probably move it to your data lake.

Learn more about Azure Data Lake, Azure Data Warehouse, Machine Learning, Advanced Analytics, and other BI tools.

A Data Warehouse Glossary-CloudMoyo

ABC of Cloud Data Warehousing Terms- A Glossary

Data Warehouse, also known as enterprise data warehouse, is considered as one of the core elements of BI (Business Intelligence). Data warehouse is a system or means for reporting and data analysis and also supports the decision-making process. The process of planning, constructing, and maintaining a data warehouse system is called data warehousing.

Now, to have an in-depth knowledge of what ‘cloud data warehousing’ is all about, you need to first know and understand the most important aspects and practical details of the concept. Listed down are the terminology that you must know to be a master of cloud data warehouse system. Enjoy diving in-


Ad-Hoc Query:  A query or a command which is created for specific purpose when issue remains unresolved with predefined datasets. Contrary to a predefined query, an ad-hoc query gives different results depending upon the variable. The output value cannot be predefined. It is created dynamically based on user’s demand.

Aggregation: Facts aggregated from a raw level to higher levels in different dimensions in order to mine business–related data or service from it with faster approach. For selected dimensions, facts are summed up from the original fact table. This speeds up the query performance. The aggregated facts or summaries are done over a specific period.

Attribute:  It refers to any particular column or distinct type of data in a dimension table.

Attribute Hierarchy: There are 3 different levels of hierarchy of Attribute members: Leaf level (different attributes), Intermediate level (Parent-child hierarchy) and the Optional level (sum of the value of the attribute hierarchy members).

Application Gap: A recorded difference between some parts of a business prerequisite and application system’s ability to meet the necessary requirements.

Automatic Encryption: The automatic encryption of data to keep it unaffected from the external influence. Data encryption helps translate data into readable text or code. This allows the users who have access to secret key to read, use and analyze it.


Backup and recovery strategy: A strategy that prevents loss of important business data from the enterprise hardware or software due to any technical or natural faults.

Baseline: A baseline is a point that signifies deliverability of any project. It is a milestone or a point that can calculate what changes are to be made in the project.

Business Metadata: The data of knowledge composed for the users, which helps them understand the data warehouse. It concentrates on what data warehouse consists of, data source, data relevance, etc…

Business Intelligence: The objective of Business Intelligence is to provide data related to business operations which helps in making right decision at the right moment.

Backend Tool: It is a software that helps in the extraction process, typically resident on both the client and the server, that assists in the production data extract process.


Conformed Dimension: It is a dimension that has the same meaning when being referred from different fact tables. Conformed dimensions allow facts and measures to be categorized and described in the same way across multiple facts and/or data marts, ensuring consistent reporting across the enterprise.

Case tools:  A complete set of application development tools and CASE (Computer-Aided Systems Engineering) that help in the development of software.

Category:  An architecture for managing, indexing, and representing a dimension of a multidimensional cube.

Central Repository: A place where a set of documentation are saved, adapted, customized, reformed, or enhancements designed to alleviate the reformation of accomplished work.

Client/Server: A kind of technical structural design that links many workstations or PCs (Personal Computers) to one or more servers. Generally, Client manages the UI, probably with some local data.

Cluster: A platform or a channel of saving a set of data from multiple tables, when the data in those tables holds similar information accessed concurrently.

Column: A process of implementing a part of data (sort by date, character, format) within a table. It can be optional or mandatory.

Catalog: A module of a data dictionary that explains and manages number of aspects of a database (say folders, dimensions, functions, queries, etc…)

Cross Tab: A kind of multi-dimensional report that exhibits values or measures in cells created by the intersection of two or more dimensions in a table format.

Cloud computing: Cloud Computing mainly refers to the provision of IT resources by a provider via the Internet (fixed and mobile).

Cube: It is a multi-dimensional data matrix that has several dimensions of independent variables and measures (dependent variables) that are developed by an OLAP (Online Analytical Processing System). With multiple levels, each dimension is organized into a hierarchy.

Cloud Analytics: Cloud analytics is a service model in which sub-elements of the business intelligence and data analytics process are provided through a public or private cloud. Cloud analytics applications and services can be available as a subscription-based or utility (pay-per-use) pricing model


Data Analytics: Data analytics is the process of querying and interrogating data in the pursuit of valuable insights and information.

Data link: Created by UCSD’s Data Warehouse team, Data Link is a web-based tool. It gives a sheer knowledge of the data, data history, database, tables, available SQL queries and fields used in DARWIN.

Data Mining: The practice of identifying the relationship and pattern of set of data that involves various techniques.

Data Refresh: The process by which all or part of the data in the warehouse is replaced.

Data Synchronization: Keeping data in the warehouse synchronized with source data.

Data Mart: Data marts contain a subset of organization-wide data that is valuable to specific groups of people in an organization.

Dimension: Information and data of same type. For example- Time Dimension type will contain information of year, month, day, and week.

Dimensional Model: A type of data modeling suited for data warehousing. In a dimensional model, there are two types of tables: dimensional tables and fact tables. Dimensional table records information on each dimension, and fact table records all the “fact”, or measures.

Drill Across: Data analysis across dimensions.

Drill Down: Data analysis to child attribute used to zoom in to more detailed data by changing dimensions

Drill Through: Data analysis that goes from an OLAP cube into the relational database.

Drill Up: Data analysis to a parent attribute.

Data Map: A technique for creating a balance or data elements mapping between 2 different data models. For wide variety of data integration works, data mapping is used as the first step. Data mapping is used to create a match between data sources and target database element.

Data Lake: Data lake is storage of massive amount of data in its native format until the rise of its requirement. It uses flat architecture for storage.

Data Protection: It is a process of safeguarding important information from corruption and/or loss.

Decision Support System: A software system used to support decision-making processes within an organization.

Data quality: Quality that determines the reliability of data. High-quality data needs to be complete, accurate, available and timely.

Data cube: A data cube helps us represent data in multiple dimensions. It is defined by dimensions and facts. The dimensions are the entities with respect to which an enterprise preserves the records.

Data cleansing: With the help of data cleansing it is possible to remove and to correct data errors within a database or other information systems. Examples for such procedures are the erasing of data duplicates or the compression of similar information.

Data Governance: A structured and standard process of maintenance of data and transformation of data into valuable, practical and functional information.

Data Mashing: A process of reconsolidation and merging the new data with the already existing content.

Data migration: A process of transferring data between storage devices or computer systems – preferably without disrupting active applications. This process is usually achieved programmatically with database queries, developing custom software or with external migration tools.

Data Vault: A method that enables a process and approach to modeling your enterprise data warehouse.

Denormalize: Denormalize means to allow redundancy in a table so that the table can remain flat.

Degenerate Dimensions: A dimension key with no attributes (or actual dimension table), such as an invoice number etc.

Data Dictionary: A part of a database that carries meaning of data objects.

Data Extraction: The process of pulling data from operational and external data sources in order to prepare the source data for the data warehouse environment.

Data Integration: The movement of data between two co-existing systems. The interfacing of this data may occur once every hour or a day, etc.

Data Integrity: The data quality that rests in the database objects. Criteria that users verify when analyzing the data reliability and data value.

Data Replication: The process of creating a replication or copy of data to/from the sites to enhance the local service response times and availability.

Datastore: A temporary or permanent storage concept for logical data items used by specified business functions and processes.

Data Scrubbing: The process of manipulating or cleaning data into a standard format. This process may be done in conjunction with other data acquisition tasks.

Data Source: It’s accessed during the process of data acquisition.  An external system or an operational system or third-party system that enables data to gather the information required by the uses.

Dimension Table: A table that contains discrete values in a spreadsheet.

Distributed Database: A physically located database on multiple computer processors is called as distributed database. It is linked through some means of communications network. An essential feature of a true distributed database is that users or programs work as if they had access to the whole database locally.


Enterprise Resource Planning: Enterprise resource planning (ERP) is a system that integrates and manages internal and external information in a organization. A ERP-System is used for a company to maintain and use data in a flow for the organization to use the advantage of being connected to vendors, customers etc

Executive Information System: A crisp collection of a high-level, customized graphical view of the enterprise data enabling management to scan/view the overall status of the business.

Entity Relationship Model: A part of the data model of business that comprises multiple Entity Relationship Diagrams.

External Data Source: An external data source of the data files/folder or system that is catered to the client.

Extraction, Transformation and Loading (ETL) Tool: ETL Tool is a software that is used to extract data from a data source like a operational system or data warehouse, modify the data and then load it into a data mart, data warehouse or multi-dimensional data cube.


Fact Table: Structured by a composite key, each of whose objects is a foreign key extracted from a dimension table, a fact table is the central part in a star join schema.

Forecasting: Forecasting is a prediction of the actual business condition presented with statistical methods.

Foreign Key: A foreign key is a column or a set of columns in a table whose values correspond to the values of the primary key in another table. In order to add a row with a given foreign key value, there must exist a row in the related table with the same primary key value.

Field: A means of implementing an item of data within a file. It can be in character, date, number, or other format and be optional or mandatory.

File Transfer Protocol (FTP): The physical movement of data files between applications, often across sites.

Format: The type of data that an attribute or column may represent; for example, character, date, number, sound, or image.


Granularity: The level of detail of your data within the data structure.


Hierarchy: The logical structure tree or managing data according to its level. The individual level of the hierarchy is further denoted as categories. The individual elements within a level are referred to as categories

Hybrid OLAP: Also known as HOLAP is the combination of the technologies called ROLAP (Relational OLAP) and MOLAP (Multidimensional OLAP). It allows storing part of the data in a multidimensional database and another part of the data in a relational database and allows using the advantages of both technologies.


Indexing: An index is a link between one table and another for rapid access to the rows of a table based on the values of one or more columns in another table.

Implementation: The installation of an increment of the data warehouse solution that is complete, tested, operational, and ready. An implementation includes all necessary software, hardware, documentation, and all required data.

Information Access Model:  Information Flow Model is a model that visually depicts information flows in the business between business functions, business organizations and applications.


Junk Dimensions: Attributes which are not a part of any current dimension tables or a fact table.

JSON: It is a semi-structured data format that can be used in multiple apps, but has become more common as a format for data transmission between servers and web applications or web connected devices.



Legacy System: A current repository system of data and processes.

Link Test: A test to discover errors in linked modules of an app system.

Logical Data Warehouse Architecture: It is a framework which sketches the complete functions and elements of a strategic warehouse. This includes warehouse management, ETL components, metadata repository, data classes, relational and multidimensional databases, etc.


Metadata: Information/description about the particular data.

Metric: A measured value. For example, total sales is a metric.

MPP: The acronym MPP or Massively Parallel Processing is the synchronized processing of a particular program by number of processors that operate on multiple parts of the program, every processor using its individual operating system and memory.

Middleware: A system that makes it easier for the software to exchange data between end users and databases.

Mission Critical: A system that if it fails effects the viability of the company

MOLAP: Multidimensional OLAP system that stores data in the multidimensional cubes.


Natural Hierarchy: In general, a hierarchy is a collection of levels based on attributes. With that said, there are existing natural hierarchies, like country, state and city as well as year, month, week, and day also known as time hierarchy. These two examples represent a natural relationship related to attributes. This type of hierarchy has only one parent and also indicates the member attribute above it.  This gives the idea to develop more user-defined-hierarchy i.e. more individual hierarchies.

Normalization A technique to eliminate data redundancy.

Non-Volatile Data: Data that is static or that does not change. In transaction processing systems, the data is updated on a regular basis.


OLAP:  Online Analytical Processing (OLAP) is an online data recovery approach and its analysis to disclose the current trends and statistics of the business, which is not directly visible in the data that is retrieved from a data warehouse directly. This process is also known as multidimensional analysis.

Operational Datastore (ODS): A database designed to integrate information from different sources for add-on data operations for reporting and operational decision support.

Operational Data Source: The current operational system, which encompasses the data source for the ETL process (Extracted, Transform and load to the data warehouse database objects.)


Primary Index: An index used to improve performance on the combination of columns most frequently used to access rows in a table.

Primary Key: A set of one or more columns in a database table whose values, in combination, are required to be unique within the table.

Problem Report: The mechanism by which a problem is recorded, investigated, resolved and verified.

Parallel query: A process by which a query is broken into multiple subsets to speed execution

Partition: The process by which a large table or index is split into multiple extents on multiple storage areas to speed processing.

Proof-of-Concept: An approach, usually coming from an experiment for demonstrating immediate business concept, proposal, its design, etc. are feasible.


Query link: QueryLink is a Web-based tool for easy access to Data Warehouse information without knowing a programming language.

Quality Review A review used to assess the quality of a deliverable in terms of fitness for purpose and adherence to defined standards and conventions.


Record: A record is an entry in a file, in a non-relational database system, containing of data of each element, which together cater complete details of an element the data required by the system.

Referential Integrity Constraint Rules that specify the correspondence of a foreign key to the primary key of its related table.

Refresh: An approach that gives you an option to update the database objects of the data warehouse with fresh data. This procedure is monitored through the data warehouse management processes and appears on a scheduled basis after the initial load.

Relational Database Management System (RDBMS): A DBMS (database management system) in which data can be seen and manipulated in a tabular form . Data can be sorted in any order and tables of information are easily related or joined to each other.

Relational Online Analytical Processing (ROLAP): OLAP software that employs a relational strategy to organize and store the data in its relationship database.

Reporting Database A database used by reporting applications. Reporting databases are often duplicates of transaction databases used to off-load report processing from transaction databases.

Repository: A tool for storing any facts, figure or info about the system at any point in its life-cycle. This is used for mainly for recovery, extensibility, integrity, etc…

Replication: The process of copying data from one database table to another.


Schema: An information model implemented in a database is called Schema. It may be a logical schema, which may not include any optimization. It may be a physical schema that includes optimization or customization.

Scalability: The capability to increase numbers of users and volumes of data to the data warehouse system. This is an important ability for the technical architecture of the cloud data warehouse.

Snowflake Schema: A common form of dimensional model. In this, number of hierarchies in a dimension can be extended into their individual dimensional tables.

Star Schema: A common form of dimensional model. In a star schema, a single dimension table represents each dimension.

Snapshot: Specifically defines a fact table that denotes the state of affairs at the end of each time period.

SQL (Structured query language): A standard language for creating, modifying, and querying an RDBMS.

Start Schema: A collection of dimensions joined together with a single fact table that is used to construct queries against a data warehouse.

Summarization: The process by which data is summarized to present to DSS or DWH users.

SaaS: Software as a Service allows software to get license on a subscription basis. It is a software licensing and delivery model in which software centrally hosted.

Slice and dice: It is the typical description for data access, equally via any of its dimensions


Table: A tabular view of data, on a relational database management system, defined by one or more columns of data and a primary key. A table populated by rows of data.

Tablespace: A logical portion of a database used in allocating storage for table data and table indexes.

Target Database: The storage of the source data, in a data warehouse database object, once it is extracted, transformed and transported.

Transmission Control Protocol/Internet Protocol (TCP/IP): It provides a link to transmit data across the web.

Twinkling Database: In this, the data you are trying to query is not stable. It is constantly changing.


Uniform Resource Locator (URL) is the path information in an HTML-coded source file used to locate another document or image.

Usability: That quality of a system that makes it easy to learn, easy to use and encourages the user to regard the system as a positive help in getting the job done.

Unbalanced Hierarchies: An unbalanced hierarchy exists if any branches of the hierarchy descent to different levels. In other words, in an unbalanced hierarchy, not every leaf in the hierarchy has the same level.

User-defined-hierarchy: A hierarchy of attributes, which is used to manage the members of a dimension into hierarchical structures by catering navigation paths in a Cube. For example, take a dimension table that supports three attributes, named Year, Quarter and Months. The Year, Quarter and Month attributes are used to construct a user-defined-hierarchy, named Calendar in the time dimension


View: A means of accessing a subset of data in a database

Virtual Warehouse: The view over an operational data warehouse is known as virtual warehouse. It is easy to build a virtual warehouse. Building a virtual warehouse requires excess capacity on operational database servers.


World Wide Web: The World Wide Web is a hypermedia application used for access of data over the Internet. The WWW is based on the HTML standard of marking up documents.




A beginner’s guide to Microsoft’s Azure Data Warehouse

Your business data is extremely POWERFUL, only if you are able to use it properly– to generate valuable and actionable insights. However, it is also imperative to organize and analyze it well. A recent report says, less than 0.5% of the business data is actually stored and analyzed in a right way. As an impact, enterprises lose over $600 billion a year.

Today, the power of computing and cloud storage of business data has lifted up the demand for a data warehousing solution by businesses of all sizes. It is no more a large capital expenditure; indeed, it has become a one-time investment on the implementation of data warehousing system and can be deployed in no time. This allows any business to access their structured data sources and thus, collect, query and discover insights from it.  Microsoft has introduced Azure SQL Data Warehouse that has come as a permanent and effective product in the data platform ecosystem.

Microsoft’s Azure SQL Data Warehouse is a highly elastic and scalable cloud service. It is compatible with several other Azure offerings, for instance, Data Factory and Machine Learning and with various SQL Server tools and Microsoft products. Azure’s SQL based Data warehouse has the capability to process huge amount of data through parallel processing. Being a distributed database management system, it has overcome most of the shortcomings of traditional data warehousing systems.

Before handling the logic involved in data queries, Azure SQL Data Warehouse spreads data across multiple shared storage and processing units. This makes it suitable for the batch loading, transformation, and serving data in bulk. As an integrated Azure feature, it has the same scalability and consistency just like other Azure services like high-performance computing.

The traditional data warehouses have two or more identical processors and consist of Symmetric Multiprocessing (SMP) machines. They have complete access to all I/O devices as these are connected to a single shared memory. A single Operating System controls and treats them equally. With growing business demand in the recent years, the need for high scalability has arisen.

Read our whitepaper on advantages of cloud data warehouse

How Azure Data Warehousing Overcomes These Drawbacks

Azure SQL data warehouse caters all demands through shared nothing architecture. The feature of data storage in multiple location enables to process large volumes of parallel data. If you are new to Azure data warehouse and want to understand it completely, you can take Azure training from experts. You will get to know about virtual networks, azure machines and more during your training.

Features of Azure Data Warehouse:

  • It is a combination of SQL Server relational database and Azure cloud scale-out capabilities;
  • It keeps computing separated from storage;
  • It can scale up, scale down, pause and resume computations;
  • Azure is an integrated platform;
  • It includes the use of tools and T-SQL (SQL server transact).

From legal to business security requirements, it shows complete compliance.

Benefits of Azure Data Warehouse

  1. Elasticity: Azure data warehouse possesses a great elasticity due to the separation of computing and storage components. Computing can be scaled independently. Even if the query is running, it allows addition and elimination of resources. 
  2. Security-oriented: Azure SQL has various security components (row-level security, data masking, encryption, auditing, etc.). Considering the cyber threats to cloud data security, components of Azure data warehouse are secure enough to keep your data safe.
  3. V12 Portability: Now, you can easily upgrade from SQL Server to Azure SQL and vice-versa with the tools that Microsoft provides.
  4. High Scalability: Scalability is high in Azure. Azure data warehouse scales up and down quickly according to the requirements.
  5. Polybase: Users can query across non-relational sources with through Polybase.

Different Components of Azure Data Warehousing and Their Functions:

  1. Control Node: All connections and applications communicate with the front end of the system–Control node. From the data movement to computations, the control node coordinates everything required for running parallel queries. To do this, all individual queries are transformed to run in parallel on various Compute nodes.
  2. Compute Node: As the compute nodes receive the query, it is further stored and processed. Even the parallel processing of queries takes place with multiple compute nodes. The results are passed back to the control node as soon as the processing completes. Then the results are collected, and the final result is returned.
  3. Storage: Azure Blob storage can store large amounts of unstructured data. Compute nodes read and write directly from Blob storage to interact with data. Azure data storage is expanding transparently. The storage is resistant to flaws. It provides strong backup and restores data in no time.
  4. DMS: Windows provides the Data Movement Service, and it runs alongside SQL databases on all nodes. This moves the data between nodes. It forms the core part of the whole process as it has an important role to play in data movement for parallel processing.

 Azure Data Warehouse Structure and Functions

  • Being a distributed database system, it is capable of shared nothing architecture.
  • The data is distributed throughout multiple shared, storage and processing units.
  • Data storage in Azure data warehouse is a premium locally redundant storage layer.
  • Compute nodes on top of this layer execute queries.
  • As the control node is capable of receiving multiple requests, they are optimized for distribution to allocate to various compute nodes to work parallel. 

When you need massively parallel processing (MPP), Azure SQL Data Warehouse is the ultimate solution. Unlike the on-premises equivalent, Azure SQL Data Warehouse solutions is easily accessible to anyone with a workload using the familiar T-SQL language.

If you are looking to harness this wonderful data warehousing solution for your business, a Microsoft Partner like CloudMoyo can help. From evaluation, requirements and assessment phase, to data warehouse platform selection, architecture, integration, data management and further support, CloudMoyo’s brings to the table expertise, flexibility along with long term commitment for excellence. Get started today with our 5-day Azure assessment workshop for your organization!

Future of Cloud data Warehouse - CloudMoyo

The Future of Cloud Data Warehouse: Where is it going?

The emergence of the data warehouse transformed the business information management landscape which was previously restricted to manual methods, complex & unwieldy spreadsheets and was generally inaccessible to the general users. Its exponential and rapid growth has made companies realizes the value of the data they generate. This gave rise to the environment of cloud data warehouse.

With the fast evolution of data warehouse, most forward-thinking enterprises migrated their data and systems to cloud to expand their network and markets. The birth of an on-premise data warehousing helped the companies in filtering the data, storing and organizing it, and making it easily accessible for the business users.

The Puff of ‘Big Data’

Lately, the concept of ‘Big Data’ became the topic of discussion, concerning the importance of data warehouse. As Ian Dudley defines it “Big data has volume, velocity and variety: it is large, grows at a fast rate, and exists in many different physical formats (video, text, audio, web page, database, etc.). It is not possible to apply traditional warehousing techniques to this sort of data.” This not only reveals the relevancy of data warehouse but also uncovers how a modern data warehouse must look like.

Likewise, the development of data warehouse also uncovers the immediate way you are currently practicing: the requirement for an intense, easy-to-use and economical data warehouse created for the cloud to bank all your data in one-single point and use and analyze it later. Therefore, the modern data warehouse came as an effective data solution.

Managing Data Today With Data Warehouse

The needs of the new trends of data storage came as a blockade for traditional data warehouse. The elements that the users look for in any data warehouse are: real time answer to the query, digital data storage, structured data, increasing data volume, new types of data and data sources, advanced deployment models in the cloud/hybrid, machine learning and advanced analytics.  Hence, to support these particular elements, the modern data warehouse was designed. This helps in managing the unstructured/relational data. A modern data warehouse helps manage Big Data while handling fast queries expectations from the users. Through one query model, it makes an easy interface with all kinds of data.  

Read 5 benefits of moving your on-prem Data Warehouse to the Cloud

A Modern Data Warehouse Helps in Solving Core Business Issues

The modern data warehouse is changing the face of Big Data and Business Intelligence by providing an easier yet effective and all-powerful way to achieve the requirements of the new trends. Users can stream data in real-time by bridging the stored data from past with the live data.

In previous times, data analytics and business intelligence happened to take place in two different sections of the company. Unlike today, only historical data were accessible for analysis. In the current scenario, it’s different. Businesses will fall slow and underperform if it comes to just look the data from past and analyze it. Hence, the technicalities of modern data warehouse came with some extra spaces to tackle these business issues.

  • Advanced structure for storage: Data lakes ditch the traditional form of storing the data in hierarchical folders. Instead of that, it has a new and advanced flat architecture for storing (raw) data in its unrefined form. It can be stored in its organic form until needed by the users.
  • Faster data flow: The modern data warehouse allows data fragmentation like access and analysis of data across the enterprise in real time. This helps in maintaining the agility model and advocates data flow with relatively faster approach.
  • Sharing and storing data through IoT: With the advancement of Internet of Thing, sharing and storing of data has become easier. Hence, IoT has changed the face of streaming of data. Businesses, customers, users store data across multiple devices and make it available for other user too.

The cloud data warehouse offers unparalleled flexibility. No longer do organizations have to compromise on value based on how data is entering their system.

Enter Azure SQL Data Warehouse

– Microsoft’s Modern Cloud Data Warehouse Solution

Introduced in 2015, Azure SQL Data Warehouse is a massively parallel processing (MPP) cloud-based, scale-out, relational database capable of processing massive volumes of data. A highly elastic- Azure SQL-based data warehouse is a completely organized and well-managed data warehouse. It does not take more than a few minutes in setting it up and a few seconds to scale its capabilities. It separately scales the capacity of storage and that of the computing. This helps the user to accordingly scale up or scale down the data warehouse, for complex analytical workload or for the archival scenarios, respectively. Hence, it is cost-effective and caters modern data warehouse solutions.

How Do You Get Started With Azure Cloud Data Warehouse

As a Microsoft Gold Partner, CloudMoyo has the expertise in leveraging Azure data platform-as-a-service to offer a complete suite of data warehousing solutions. Our experience starts with development of a data warehouse, implementation of full data warehouse lifecycle with verified methodologies and data warehouse maintenance of its operation and support.

CloudMoyo provides a data warehousing solution that includes an Operational Data Store (ODS) and data mart development, data lake analytics,  etc.

To book a 5- day customized assessment/ workshop to address your data needs, just fill this form and we’ll do the rest!

The Azure Data Lake is a robust holding tank for all your raw unstructured data - CloudMoyo.

A deep dive into the Microsoft Azure Data Lake and Data Lake Analytics

Today, large enterprise organizations are struggling with an ocean of data. From online shopping analytics to Internet of Things (IoT) sensor data, the modern IT team is inundated with raw or semi-raw data captured from every side of the organization. These entities have begun dumping this raw data into a holding tank called the data lake until they can make use of all of the non-defined, schema-less information. Data that hasn’t yet reached its full potential can now be housed in Microsoft’s Azure Data Lake, a robust cloud-driven repository for big data. This article explains what the Azure Data Lake is and how it can be used for data analytics on a massive scale.

What is the Azure Data Lake?

The Azure Data Lake is a giant computer repository of information stored in the public cloud. For organizations attempting to house data on-premise, the cloud offers a secure, virtually unlimited solution for the big data we’re generating today.

The backbone of the Azure Data Lake is the Hadoop File System, which ensures massive computing of petabyte-sized files. But the Azure Data Lake isn’t meant just as a Grand Canyon-sized holding tank; it also enables data scientists, marketers, and analysts to run data lake analytics to begin to understand the data as a first step toward using it effectively.

Microsoft now offers the Azure Data Lake along with data visualization and data lake analytics tools that can change how enterprise organizations handle their most basic processes around the capture and management of data. Together, these tools provide real business insights for enterprise organizations in any industry or market.

Azure Data Lake – Business Benefits

The Azure Data Lake helps streamline the efficiency of your data storage by allowing enterprise organizations to quickly query, process, and store data. One benefit is that the Azure Data Lake is housed in the cloud, which means it is incredibly scalable and flexible. Beyond that, the data lake analytics you need can run concurrently; executions can effectively occur across hundreds of data terabytes more quickly than you’ve ever experienced, allowing you faster access to key business insights.

Azure Data Lake also integrates effectively with data warehouses or other platforms so you can move data in its raw form to a more structured environment such as a data warehouse.

Azure Data Lake analytics - CloudMoyo






Azure Data Lake and data lake analytics are the one-two punch for big data

Azure Data Lake and Data Lake Analytics

The Azure Data Lake allows high throughput data lake analytics of all your raw and semi-structured data. It is the perfect solution for organizations seeking to meld a data lake with a data warehouse. Together, Azure Data Lake and data lake analytics allow for real-time actionable insights moving at the speed of your business.

Are you interested in learning more about Microsoft Azure cloud analytics services and how they can give your business a competitive advantage?

We would be happy to answer your questions, and we encourage you to contact us anytime.

Traditional V/S Cloud Data Warehouse - CloudMoyo

8 reasons why a Cloud Data Warehouse outshines On-Premise Data Warehouse

Traditional Data warehousing has hit a roadblock. Most organizations have ancient information management systems typically built in an age where inflexible systems working within solos were sufficient to address data needs of that era- limited data sources, infrequent changes, lesser volume of transactions and low competition. But today, the same systems have been rendered ineffective with the splurge in data sources as well as volumes. What’s more is that today, to remain competitive in a fast changing landscape, access to near real-time or instantaneous insights from data is necessary. Simply put, the legacy warehouse was not designed for the volume, velocity, and variety of data and analytics demanded by the modern enterprise.

Below, we have tried to capture in a nutshell how the modern or cloud data warehouse differs from traditional one.

Traditional Data Warehouse Modern Data Warehouse
Not designed for the volume, velocity, and variety of data and analytics Designed for sheer volume and pace of data.
Accessible only to the largest and most sophisticated global enterprises Can be used by individual departments like marketing, finance, development, and sales at organizations of all types and size
Prohibitively expensive and inflexible Affordable to small and mid-sized organizations, very easy to adapt dynamic changes in data volume and analytics workloads
Slow batch processing, crippled business intelligence Data available immediately and at every step of modification, supporting data exploration, business intelligence and reporting
Inability to handle growing numbers of users No Limitations on number of users
Updated analytics on a weekly or daily basis and no accessibility easily Data insights can be always up to date and directly accessible to everyone who needs them
More focus on data management Empowers enterprises to shift their focus from systems management to analysis.
Limitations of an approach and architecture where changes are infrequent and carefully controlled Operates painlessly at any scale and makes it possible to combine diverse data, both structured and semi-structured


The emergence of cloud has been monumental in modernizing the data warehouse. Cloud data warehousing is a cost-effective way for companies to take advantage of the latest technology and architecture without the huge upfront cost to purchase, install, and configure the required hardware, software, and infrastructure.

To conclude, on-premises workloads will continue to shift to the cloud. In the days to come, the cloud data warehouse will replace the on-premises warehouse as the main source of decision support and business analytics. Azure SQL Data Warehouse, a cloud based data warehouse hosted on Microsoft Azure is capable of processing massive volumes of data and can provide your business the speed & scale that it needs to manage enterprise data.

An engineering giant recently found out the benefits of Azure working with a premier consulting partner like CloudMoyo. Click here to find out more.

At CloudMoyo, we help you migrate your data platform to the Azure cloud, as well as help build customized solutions in Azure to make the most out of your data. To know more, book a 5-day Azure Assessment workshop to jointly build the strategy and roadmap to move to a cloud-based data deployment

5 Reasons You Should Move Your Data Warehouse To The Cloud

5 benefits of moving your On-Prem Data Warehouse to the cloud

Now-a-days, a lot is being written about emerging technology and invariably, ‘Cloud’ always gets a mention along with Big Data and Mobile. We agree that moving to the cloud isn’t as easy as turning on a fan, and when it comes to data management, business intelligence or reporting, it definitely isn’t a cakewalk given that there are perceived problems such as performance, security, lack of control etc. However, modernizing your data platform by moving your data warehouse or business intelligence (BI) solution to the cloud is worth trying out. Here’s why-

  1. Security– Let’s start with the most hated but high important aspect of solutioning – security & privacy. For ages, having your data on the cloud as against on-premises has raised questions about security, data breach & privacy issues. However, Microsoft Azure, the world’s most robust cloud platform, places a high tag on security. Its data platform tools are tightly coupled with Azure Active Directory (AAD) to provide authorization and data-level security, encryption of data in motion and at rest, enable IP restrictions, auditing, and threat detection. Azure presents the most comprehensive compliance coverage amongst cloud providers. It has more certifications than any other cloud provider, and is an industry leader for customer advocacy and privacy protection with its unique data residency guarantees.
  2. Economy- The cloud model lowers the barriers to entry—especially cost, complexity, and lengthy time-to-value. Cloud pricing differs greatly compared to on-premises infrastructure. You have to take into consideration licensing, man-power, hardware, real estate, electricity, support cost, security, deployment cost and depreciation. All this comes with fixed capacity. But with the cloud, you get to pay for what you use and can even vary the desired configuration and performance levels. And it isn’t just the time and money; Cloud deployment can also free up your resources that otherwise would have been dedicated to managing the new environment
  3. Transformation– Traditional data warehouses consist of data models, extract, transform, and load processes, and data governance, with BI tools sitting on top. Instead of doing things the old way, which includes structuring, ingesting and analyzing, enterprise data warehouses need to flip the paradigm and ingest, analyze, and structure by utilizing the cloud, data lakes, and polyglot warehousing. You need to think of your data warehouse not as a single technology but as a collection of technologies.
  4. Agility– Many business functions, hitherto not associated with BI, have taken to data analytics for justifying spends, analyzing performance etc. It will be unproductive for these lines of business to wait for central IT to provision a data warehouse for them so they can start analyzing their data. The cloud offers a relatively quick as well as robust solution to cater to these warehousing needs. On the contrary, for on premise infra, procurement as well as deployment cycles are very long. Add to that the pain of going through upgrades every 2-3 years.
  5. Intersection with Big Data– Big data has empowered the world to tap any kind of unstructured data sources to gain insights. Cloud data warehousing can be a bridge for bringing the world of structured data from legacy on-premises data warehouses together with these newer big data sources.

To conclude, on-premises workloads will continue to shift to the cloud. In the days to come, the cloud data warehouse will replace the on-premises warehouse as the main source of decision support and business analytics. Azure SQL Data Warehouse, a cloud based data warehouse hosted on Microsoft Azure is capable of processing massive volumes of data and can provide your business the speed & scale that it needs to manage enterprise data.

At CloudMoyo, we help you migrate your data platform to the Azure cloud, as well as help build customized solutions in Azure to make the most out of your data. To know more, book a 5-day Azure Assessment to jointly build the strategy and roadmap to move to a cloud-based data deployment.


Microsoft Azure – a review of the cloud platform

“Infrastructure is a big selling point for Amazon Web Services, but Microsoft is an important competitor, especially for clients who are already using the Microsoft stack. They can connect their domains seamlessly in these cases. Hybrid solutions work very well with the Microsoft Azure stack.” says Venu Machavaram, Director of Cloud Architecture at CloudMoyo in an interview with Clutch. Clutch is a Washington, DC-based research firm focused on the technology, marketing, and digital industries providing independent, quantitative, and qualitative analysis on leading services firms to support procurement decisions in small, medium and large enterprises.

CloudMoyo helps modern enterprises define their path to the Cloud and leverage the power of data driven insights. CloudMoyo utilizes Microsoft Azure in a hybrid setting, and often is subject to compliance regulations, such as HIPAA (Health Insurance Portability and Accountability Act). They state that Microsoft Azure provides easier infrastructure implementation, and organizations can see a positive offset in operational costs within the first five years. CloudMoyo recommends the Microsoft Azure platform to organizations familiar with the Microsoft stack. Venu talks to Clutch about his experience of working on the Azure platform

What is the business challenge a company faces that initiates the need for this platform?

Companies are concerned with costs as well as getting the right resources for these operations. Even though the cloud is something that people talk about constantly, the right skillsets aren’t implemented everywhere yet. The time necessary for migrating to the cloud and defining new business processes are also primary concerns. There are enterprises which have been in the market for 50-100 years. They have established processes and they can be uncertain in terms of how such a change will affect them.

What is the process for implementing Microsoft Azure?

Legacy systems I’ve seen had been grown organically through a period of 15-20 years. If someone will move to the cloud, the reason will likely be reducing IT operational costs. The typical way to do this move is through a lift-and-shift. If Microsoft is chosen as the solution, it will be a team process which will be implemented easily with the domain connectivity offered. Operational costs won’t be offset within the first two years, but if the job is done correctly, it can happen within five years. It’s also important to note that such a move cannot be done all at once. Performance testing and the volume of the data itself are the factors to consider. Once the team is confident that the move can be done seamlessly, they can proceed.

The way in which data is stored can be hybrid and it varies from organization to organization. IT departments typically focus on cost from an operational perspective, experimenting with various apps until they are certain that a move to the cloud is possible.

Once a hybrid cloud solution has been put in place for the legacy systems, the company can ramp up the right skills and slowly start learning how to design and architect their solutions for moving forward. Any new development projects will then be made exclusively with a focus on cloud implementation, and within a couple of years, the teams will be completely ramped up for the new skills required.

In what scenario would you recommend Microsoft Azure over other platforms?

Infrastructure is a big selling point for Amazon Web Services, but Microsoft is an important competitor, especially for clients who are already using the Microsoft stack. They can connect their domains seamlessly in these cases. Hybrid solutions work very well with the Microsoft Azure stack.

The Microsoft solution is not fully realized within their Software as a Service [SaaS] offerings. There is a lot of cost involved in bringing a platform to their cloud system, as well as the right skills, team, and architecture. Businesses considering Microsoft Azure as a solution would likely take this factor into account. Microsoft should bring some simplicity in their services, making a way to seamlessly connect SQL servers through the cloud, for example.

Are there any software features/tools that you were really impressed by?

Power BI is one example of a very successful SaaS product from Microsoft. Office365 and Lync are also good examples of valuable products they offer. From our perspective as an analytics company, I see a lot of potential in the Power BI and SharePoint platforms.

Tableau is the main competitor to Power BI on the analytics side. Analytics are a two-part operation: visualization and data management. Microsoft has the right tools in place for data management, and they will continue to progress throughout 2016 in their ability to move data to the cloud. Visualizations can also be made locally though, if security is a concern.

Once Power BI picks up, business intelligence analysis can be done within the server, together with the SQL data warehouses. Businesses are open to these solutions. The only concern is the way in which data is secured. I definitely see potential for growth in this segment for Microsoft, although they are a little late to arrive in the cloud market.
Looking back, are there any areas of the platform that you feel could be added or improved upon?

Right now, I’m not assessing Microsoft Azure so much from a technical point of view. The biggest challenge for them is expressing a clear message in the market in order to stand out from their competition. Sometimes, even though a company may be offering the right solution, their message may not be coming out well. They’re also doing a catch-up game in certain areas, like offering seamless backward compatibility with certain platforms. Migration capabilities offered within SharePoint would be one example.

This interview is part of a detailed review on Microsoft Azure published on Clutch. Read the entire review here.

To explore CloudMoyo’s Data Warehousing Solutions, click here.


Compliant Healthcare Cloud – leveraging the Microsoft Azure Platform

HIPAA-compliant cloud storage implements the guidelines of the U.S. Health Insurance Portability and Accountability Act (HIPAA). These guidelines ensure the protected health information (PHI) in a cloud is portable, available to healthcare practitioners, error-free, and has access control policies and standards in place.

Regulatory Environment Overview

Healthcare & Life sciences companies are quickly becoming confronted with Protected Health Information (PHI) covered by the Health Insurance Portability and Accountability Act (HIPAA). The HIPAA Security Rule establishes national standards to protect individuals’ electronic personal health information that is created, received, used, or maintained by a covered entity. The Security Rule requires appropriate administrative, physical and technical safeguards to ensure the confidentiality, integrity, and security of electronic protected health information.

Implications on IT

IT systems in healthcare & Life Sciences organizations are required to meet stringent compliance regulations as laid by GxP, CSV, CFR part 11, HIPAA etc. And since companies that can demonstrate better patient outcomes will hold a distinct competitive strength, they must know how to comply with the HIPAA / other rules or better yet, find a partner that can navigate and help them achieve this compliance. Healthcare CIO organizations have significant experience in delivering on premise compliant systems. However, developing and deploying compliant systems in the cloud is still a challenge. Healthcare organizations of all sizes can benefit from cloud services, but only if they lock down possible security leaks.

How can we help?

CloudMoyo’s Compliant Cloud Framework helps organizations build capabilities to host, develop, integrate and migrate to the cloud environment by building the right processes, tools and services, and controls. CloudMoyo can-

  • Assess landscape & select the right cloud environment
  • Choose from a set of available tools/capabilities to match their enterprise requirements, leveraging CloudMoyo’s reference architecture
  • Build business-facing applications in the cloud environment by deploying processes, tools & services, and controls to meet the requirements of GxP, CSV, CFR part 11.

CloudMoyo solutions can help organizations meet their regulatory standards while benefiting from the use of cloud applications. CloudMoyo system validation for part 11 is a detailed process and is important for quality and safety, and record integrity. The approach to part 11 requirements such as Validation, Audit Trail, Legacy Systems, Copies of Records, Records Retention has been implemented with few of the top 5 Pharmaceuticals client.

Once a company is assured that data is protected and that data safeguards are compliant to regulations, it can look to broaden the cloud’s impact in three distinct areas such as clinical trials, R&D, Consumer Engagement. By working with a healthcare-dedicated cloud partner, healthcare organizations can glean real answers from this data, now strongly secured and compliant, to drive discovery and innovation.