Data Warehouse Migration To Cloud – The purpose of this article is not to encourage you to switch to GCP. I assume your answer is that the developer has already recognized the technologies and you have decided to go to the cloud. My goal here is to share with you my recent experience migrating an on-premises datastore to GCP.
Most Traditional Enterprise Data Warehouse (EDW) is based on star or snowflake schema. This is what we call a normalized data model, and one of the most useful methods of collecting and organizing business data in situ into fact and standard tables since Edgar CODD’s articles on normal forms in the 70s.
Data Warehouse Migration To Cloud
This type of complex schema writing architecture is still important, but it was not designed for today’s business needs, especially today with unstructured big data.
Why Choose Google Cloud For Your Data Warehouse Migration
I remember in my previous life as a business controller many years ago, whenever I had to look for data to analyze, I would take a coffee break with my colleagues because I knew I wouldn’t get any response from the data system for the next half hour. . Similar to reporting, it is based on data prior to the date.
In order to be more competitive, Business becomes not only reactive but also active. This means that the information system must be able to build predictive or machine learning models and real-time analysis systems. You couldn’t achieve this kind of challenge in the old EDW.
According to International Data Corporation (IDC), a global provider of market intelligence: «By 2025, more than a quarter of the data generated in the global data sphere will be real-time in nature, and real-time IoT data will account for more than 95%. % of this. ».
BigQuery, a modern cloud-based, fully managed enterprise data warehouse (EDW), is one solution to the above limitations.
Healthcare Insurer’s Data Migration To Google Cloud Platform
A data warehouse is an inherently complex system. I agree with you that bringing to the cloud is not easy. So how do you do it safely, securely and easily.
The pre-migration phase is critical to the success of your migration. Before any migration, you should have a detailed understanding of your current environment: your applications, servers, data, schema, etc., dependencies and their requirements. This allows you to view and organize a complete directory of jobs and items to be transferred. You’ll also need to build the foundation for your site to target GCP and train stakeholders on the go.
When you decide to move to the cloud, in addition to the promise of security, high availability and scalability, you hope to make money by reducing TCO (Total Cost of Ownership) and creating business value. But what we hear from the CFO today about annual revenue is a little different.
According to Flexer’s 2020 State of the Cloud report: “organizations have exceeded their annual spend budget by an average of 23% and most estimate that 30% of dollars are wasted”. So the biggest problem in the world today is how to raise prices. This is where the idea of FinOps comes in.
The Ultimate Guide To Cloud Data Warehouse Migration
FinOps refers to a concept that supports a collaborative relationship between finance and DevOps. It ensures you get the most business value for the year by bringing together technology, business and finance experts with a new set of practices.
Cloud costing is based on cloud usage, since there is no procurement team that determines costs and approves them, we need a FinOps task force to build interactive real-time Cloud Financial Management by breaking down silos between teams and bringing best practices inside . organization through education, comparison or evangelism.
Migrating an on-premises EDW to the cloud can be difficult. One of the best ways to overcome this safely is to break activities into backward use cases. A use case here contains all the data, data processing, system and business applications needed to achieve business value. Your task, which is a collection of interrelated and shared interdependent use cases, will allow you to organize and prioritize tasks according to their importance and criticality.
The best practice for transferring workloads is to do it in a slow, iterative way. At the end of each iteration, the use case can be moved, tested and validated. In our case, verifying the success of each migration application could be, for example, verifying that the business applications are properly prepared to receive the migrated data.
Real Time Data Pipelines For Google Cloud Sql
An EDW scheme is always a well-organized star or snowflake scheme. One of the best things you can do is migrate the schema as is to BigQuery and upgrade it when needed.
In your legacy environment, data is extracted from the data source, transformed and transferred to your on-premises EDW. when entering data into BigQuery, it is recommended to use Avro, Parquet, or ORC formats instead of CSV or JSON. It allows you to quickly process raw data in BigQuery.
We now know that it is possible to manage large data changes in BigQuery. So it is better to extract and load raw data (batch or stream) with Google services like Dataflow, Cloud Storage, Data Fusion, Pub/Sub… in a raw BigQuery environment and then convert it to fit a dimensional star schema or denormalized. In other words, use ELT instead of ETL if possible as shown in the image below.
The biggest challenge of moving to the cloud is cost and performance. Since we envision migrating the standard star schema as-is to BigQuery, we’re pretty sure it doesn’t work. There are several ways to do this depending on your use cases.
The Best Practices And Strategy For Migrating Data Warehouse To The Cloud
BigQuery is a column-oriented database. It is designed to support denormalized data more than normal data. But denormalization is not necessary. It depends on your usage. It is recommended to denormalize your schema if your table is large, more than 10 GB, for example, if it is not frequently updated or deleted. This will improve the performance of your queries and reduce your costs.
Most traditional EDWs are designed to support OLTP functionality, NOT BigQuery. To avoid SELECT * or multiple join queries that can affect your performance, you can handle nested and repeated fields scheme as below.
Aggregation and segmentation can help optimize costs and performance by reducing the amount of data processed by queries. Instead of checking the entire table, queries are limited to only the partitioned or aggregated table.
When you read and store data on BigQuery, functions use shared functions. By default, when you run SQL queries, BigQuery allocates slots based on the Fair Scheduler. This means that your queries will share the available slots adequately and if there is only one SQL query, it will have access to all available slots.
The Ultimate Guide For Cloud Migration
So we need to manage and prioritize tasks, because they don’t have the same process or business value.
When setting up your GCP environment, it is recommended that you divide your project based on user roles or groups. For example, data engineers with a data warehousing project may need more slot resources than product managers. This priority gives you the right to reserve a place with the project. The best rates for EDW multitasking are Flatrate, which with simultaneous speech and ELT questions have an estimated slot to use.
The above best practices are not exhaustive, depending on your use cases and design. What is true today may not matter tomorrow. For example, nested and repeated fields help improve performance and reduce costs, but are not supported by other major reporting tools such as Tableau, Power BI, … Also, flat rate is often recommended as a cost model for EDW workloads, but as a migration strategy. To build a modern cloud EDW, you may need to build a machine learning model or have multiple users and they can break your workload. In this case, Flex-rate or a combination of price types can be recommended.
Thanks for reading!!! Feel free to share what you have come across about cloud computing or any solution in the comments. Data teams in all companies have the constant challenge of connecting data, processing and working with it. They address issues such as combining multiple ETL tasks, long ETL windows tied to on-premises data storage, and increasing user demands. They must also ensure that further money laundering, reporting and analytics requirements are met by data processing. And they need to plan for the future – how will massive data be handled and how will new global teams be supported? Watch how Independence Health Group managed the migration of its Enterprise Data Warehouse (EDW) in the video above.
Migrating From On Premise Data Warehouse To Cloud: Challenges, Architecture And Use Case
Why BigQuery? On-premise data storage capacity is difficult to scale, so the main goal of many companies is to create a secure, scalable and cost-effective data storage management system. GCP’s BigQuery is serverless, highly scalable, cost-effective and a good technology that fits the EDW use case. The bulk data warehouse is designed for business agility. But moving a large, highly integrated data warehouse from on-premise to BigQuery is not a migration. You must ensure that your core systems do not break down due to database migration inconsistencies, during and after the migration. So..you have to plan your migration. How to Migrate a Database The following steps are typical
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