Concise Guide to Data Migration

Migrating data is a challenging but very important process: it’s a fundamental component of upgrading or consolidating servers, conducting server maintenance, relocating data to a data center, adding data-intensive apps like data lakes and warehouses, among other important processes.

Because of the complexity of data migration and risks associated with it, such as costly downtime or corrupted or lost data, understanding the process and having a solid data migration implementation plan is critical.

In this concise guide to data migration, you’ll learn

• The definition of data migration and reasons for migrating data;
• Data migration stages, categories, and strategies;
• Data migration key implementation steps;
• Top 3 tools for data migration.

What Is Data Migration?

Data migration is transferring data from one computing environment to another. Before the actual transferring occurs, data needs to be selected, prepared, extracted, and transformed. The data migration process also includes the validation of migrated data and shutting down the legacy data system.

Reasons for Migrating Data

As mentioned at the beginning of the article, there are many reasons for data migration, among a few of those are

• Server maintenance;
• Server/storage replacements, upgrades, consolidation;
• Application migration;
• Disaster recovery;
• Relocation to a data center.

Benefits of Migrating Data

Data migration aids companies maintain a competitive edge while improving or sustaining performance. Data storage solutions, as well as the quality of data, degrade over time, hence the steps should be taken to improve its value via upgrading or moving to another data storage solution. Data migration is extremely useful in identifying and eliminating wasteful data and reconciling databases for better use.

Data Migration vs Data Conversion vs Data Integration

Data conversion is only one step in a data migration process which includes transforming data from one format to another.

Data integration is the process of combining data from different sources and providing users with a unified view of that data.

Data Migrating Stages

There are three main stages in the data migration process: planning, migration, and post-migration. Each of those stages includes their own steps, which are described in detail below.

Planning

#big data #databases #data migration #data managemnt #data analysis process

What is GEEK

Buddha Community

Understanding Data Migration Strategy and Best Practices

Databases are the lifeblood of every business in the modern world. Data enables them to make informed and valuable decisions. Insights, patterns, and outcomes – all require the best quality of data. Therefore, when it comes time to move from an older version to a newer version of the software, there’s a need for data migration planning.

There are a lot of complexities involved in the data migration process. You can’t just copy and paste data – it’s much more complicated. You need to have some data migration strategies and best practices for the entire process. You have to create a data migration plan that outlines all the activities of the process.

Data migration takes anywhere between a couple of months to a year. It depends on the amount of data you have, the capabilities of your legacy system, and the compatibility with the new system. While there are data migration tools and software that make the work easier, you need to have a data migration checklist for beginning the procedure.

In this article, we will look at the different data migrations strategies that assist in better managing data while moving from legacy systems or upgrading. We hope that your data migration team will get an overview of the process and the best practices that they can adopt. 

What is the Need for a Data Migration Strategy?

The primary purpose of a data migration plan is to improve the performance of the entire process and offer a structured approach to your data. Data migration policies are useful to eliminate errors and redundancies that might occur during the process.

Here’s why understanding data migration strategies is important – 

1. Prevent failure
   Data migration planning helps you to avoid failure. It outlines the problems that might occur from the beginning. Data migration should not have a casual approach – cloud data migration projects require critical attention to prevent errors and issues.
2. Define the larger scope
   By following the data migration best practices, you can define the larger scope of why you are migrating the data. Whether it’s due to the transition from legacy systems or upgrading the tools, a data migration plan enables determining what the process aims to achieve.
3. Meeting deadlines
   And budgets – all becomes possible due to strategic data migration. You, like other companies, may exceed the budget or go beyond deadlines. Data is crucial at different stages, and it needs to be available at the right moment. 

What Should the Data Migration Planning Checklist Consist of?

There are many important elements to a data migration strategy. They are critical because leaving even a single factor behind may impact the effectiveness of your strategy. Your data migration planning checklist can comprise of the following – 

• Data audit
  Before you migrate, you need to do a complete data audit. Knowing your data is more essential than anything because that will tell you about its characteristics.
• System cleanup
  You need to clean up your system with data migration software and tools to fix any issues that may arise. Third-party sources are more viable in this process.
• Data migration methodologies
  Outline the techniques, procedures, and data migration steps as you begin. Methodologies are important because they determine the success of the process.
• Maintenance & support
  After migration, there needs to be regular maintenance and checkup of the data. Data may degrade over a period of time, so it needs to be assessed for any errors.
• Data integrity
  Governance and compliance is an important part of the data migration strategy. Regularly tracking and monitoring data quality is important to assure safety from vulnerabilities.

Top Data Migration Strategies and Best Practices

Now that you have a clear understanding of why a data migration strategy is needed and what it comprises, let’s move on to the best data migration strategies and best practices. 

1. Backup your data
   One of the top data migration best practices is to backup your data. You can’t afford to lose even a single piece of data. Backup resources are essential to save your data from any mishaps that may occur during the process. Backing up your data is crucial to prevent any failures during the data migration process that may lead to data loss.
2. Design the migration
   There are two ways to design the data migration steps – big bang and trickle. Big bang involves completing the data migration in a limited timeframe, during which the servers would be down. Trickle involves completing the data migration process in stages. Designing the migration enables you to determine which is the right method for your requirements.
3. Test the data migration plan
   We can never stress enough about the importance of testing the strategy you plan to choose. You need to conduct live tests with real data to figure out the effectiveness of the process. This may require taking some risks as the data is crucial. To ensure that the process will be complete, you need to test every aspect of the data migration planning.
4. Set up an audit system
   Another top data migration strategy and best practice is to set up an audit system for the data migration process. Every stage needs to be carefully audited for errors and methodologies. Audit is important to ensure the accuracy of data migration. Without an audit system, you cannot really monitor what is going on with your data at each phase.
5. Simplify with data migration tools
   It is important to consider a data migration software that can simplify the process. You need to focus on the connectivity, security, scalability, and speed of the software. Data migration is challenging when the right tools are not available. Ensure that the software you use for data migration doesn’t take you a step back in the process.

Conclusion

As you go through the process of data migration services, understanding how the process works is an essential step. Most data is migrated when there is a system upgrade. However, it involves a lot of challenges that can be solved easily by following the best practices.

We learned the different data migration strategies that can enhance the performance of the migration process. Once the data is lost, recovering it is more of a hassle than migrating it. So to ensure that you have the right assistance in data migration, hire the experts from BoTree Technologies. Call us today!

Source: https://datafloq.com/read/understanding-data-migration-strategy-best-practices/15150

#data #data migration strategy #data migration #data migrations strategies #data migration software #data migration services

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

7-Step Data Migration Plan

This 7-step data migration plan will help ensure your data will be safe, sound, and smoothly transferred wherever you need it to.

Data migration is complex and risky — yet unavoidable for most companies’ processes. Especially now, at times of mass transitioning from on-premises systems to the cloud, companies are migrating their data to or in-between Microsoft, Google, or AWS cloud storage.

Regardless of the reasoning behind your data migration, the process and its pitfalls stay the same: downtime, data misplacement, data corruptions, losses, leaks, format incompatibilities, etc. In fact, Bloor’s data migration report shows that 84% of data migration projects overrun time or budget and 70-90% of migrations don’t meet expectations.

Of course, the severity of failed migration consequences varies depending on the company’s size, the volume and importance of data, compliance implications, and more. But no matter if you are a small-to-medium or enterprise-sized company, losing data and money due to poor migration will take its toll one way or another.

To help you avoid this scenario, we prepared a 7-step data migration plan to help ensure your data will be safe, sound, and smoothly transferred wherever you need it to. These rules apply to every type of data migration, but if you’re interested in migrating Google data specifically, read this article.

#cloud #big data #data migration #data migration automation #data migration best practices #g suite data migration

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

---

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt