Instead of starting by saying Data is the New Oil – let’s just say Data is Water. It is the most essential and crucial resource for every organization in day-to-day life, just like how water is essential for human life. If you are asking the question as to why leveraging data is needed, this article might not be for you. But if you are asking – How to leverage the data effectively then this article is definitely for you.
Everyone would say the same thing about data- many organizations are not storing and analyzing it effectively, and only when this is done can the data be leveraged well enough to give prominent results. Let’s take this step by step. Step 1- Storage of data and how it is done, this is what our article majorly talks about.
What is Cloud Data Warehousing?
Data Warehousing refers to the process of collection, management, and storage of data from varied sources that are used by the organization. It is the foundation or the core of the entire analytics or the business intelligence solutions that are used by companies in decision-making and analysis.
Then what is cloud data warehousing? This refers to the kind of database that is managed and stored in a public cloud and is normally optimized for BI and analytics. That means organizations do not need to have a physical data warehouse and can now dynamically grow or shrink their data warehouses to meet business budgets as per requirements.
Other advantages of a cloud-based warehouse include the ability of Massively parallel processing or MPP which allows high-performance queries on large data volumes and the use of columnar storage which allows data to be stored in the form of columns and not rows to make reporting run dynamically faster.
In a Data warehouse, data is collected from various sources and then cleaned, validated, summarized, and reorganized to suit the needs of the business user. It is from this layer that the data is extracted for reporting, analysis, and Business intelligence purposes.
Azure Data Warehousing
With Azure, you can move an existing on-premise SQL Server database to Azure Synapse using ELT – extract, load, and transform pipeline. Depending on whether the requirement is one-time or on-demand or based on a continuous requirement. When the requirement is continuous in addition to Azure Synapse another layer of Azure data factory is added to create an automated enterprise BI experience.
In case you are getting confused now, as to why we are talking about Azure Synapse instead of Azure Data warehousing or analytics, you should understand that the Azure Synapse analytics is the combination of the eco-system of Azure Datawarehouse and Business Intelligence needs (along with Azure ML). The added advantage of Azure synapse is that it is a run-only when-needed platform which results in amazing cost savings for the users. In short, Azure Synapse Analytics is for databases, analytics, and data engineers as it uses the ONNX format to integrate mathematical machine learning models, giving the freedom to handle more amounts of queries.
How does Data Loading work in Azure Synapse Analytics?
We’ve mentioned above that the Azure Data warehouse uses ELT or the Extract-Load-Transform pipeline. But what is so different about this is the fact that in general, data warehouses use ETL or Extract-Transform-Load process for loading the data. This difference lets Azure use distributed query processing to increase the scalability and flexibility of the resources. This ELT process combines the advantages of Data Lake and Data warehouse.
In Azure, the steps for implementing ELT are:
- Extracting the source data from various sources into text files
- Storage into Azure blob or a data lake
- Data is prepared for loading
- Data is loaded into the staging area
- Transformation of the data
- Data is inserted into the production tables
Each of these steps again would need more sub-sections and steps which we will discuss later in another article, for now, it is important to understand that using an ELT framework the data is extracted into a source system and then loaded into a dedicated SQL pool
Advantages of Azure Data Warehouse ( Or Azure Synapse)
Till now, we have just discussed in brief how Cloud data warehousing is possible with Microsoft Azure, now let’s talk a little about the advantages that Azure data warehousing has then we will talk about some of the questions that need to be thought about before choosing a data warehouse.
Azure Synapse has a cloud-native, distributed SQL processing engine that runs on MPP or massively parallel processing, which we were talking about earlier. Azure also separates the storing and the compute part of the data warehouse by separating the billing thus allowing the user the flexibility to pay as per their usage. By allowing the data to be stored in columnar format, Azure Data warehousing offers scalable compute resources that are faster to access. By combining SQL Engine, Apache Spark, Azure Data Lake Storage (ADLS), and Azure Data Factory you can not only run an Automated Enterprise BI environment but also control data warehouses and data lakes for your Machine learning requirements.
In short, Azure data warehouses are used for
- Creating a new cloud data warehouse
- Migrating from on-premise to cloud data warehouses
- Create a data warehouse solution leveraging BI & ML capabilities
- Creation of DWH solutions for web applications or solutions that need storage and retrieval service
- To connect an on-premise SQL server with an Azure-hosted Data warehouse
Other advantages of Azure warehouse include Parallel Processing and Elastic Scaling to scale computing and storage capacity as needed, In-memory column storage, Security & Protection, Market Leading Price and Performance, and integration with other Azure facilities like HDInsight, Power BI, and Azure data factory.
Questions to ponder upon
As promised we have come to the last section where we want to talk about a few things that you should ponder upon while thinking of implementing a data warehouse for your organization.
- Do you want managed services?
- What kind of data sets do you need to be working with? Complex, large, or long-running? – You might need MPP in this case
- What are the data sources that you need to be stored? Structured, or unstructured? If unstructured data might be needed you might need big-data environments like Spark or Databricks to process data
- Is there a need to separate historical data from operational data?
- Do you need real-time reporting?
- What kind of workload would be required for your operations.
These are not the exhaustive list of questions that need to be answered, but it is definitely a starting point. If you are interested in knowing more about cloud data warehousing or want it implemented please feel free to reach out to us.
