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

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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

Procedure To Become An Air Hostess/Cabin Crew

Minimum educational required – 10+2 passed in any stream from a recognized board.

The age limit is 18 to 25 years. It may differ from one airline to another!

 

Physical and Medical standards –

• Females must be 157 cm in height and males must be 170 cm in height (for males). This parameter may vary from one airline toward the next.
• The candidate's body weight should be proportional to his or her height.
• Candidates with blemish-free skin will have an advantage.
• Physical fitness is required of the candidate.
• Eyesight requirements: a minimum of 6/9 vision is required. Many airlines allow applicants to fix their vision to 20/20!
• There should be no history of mental disease in the candidate's past.
• The candidate should not have a significant cardiovascular condition.

You can become an air hostess if you meet certain criteria, such as a minimum educational level, an age limit, language ability, and physical characteristics.

As can be seen from the preceding information, a 10+2 pass is the minimal educational need for becoming an air hostess in India. So, if you have a 10+2 certificate from a recognized board, you are qualified to apply for an interview for air hostess positions!

You can still apply for this job if you have a higher qualification (such as a Bachelor's or Master's Degree).

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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

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).

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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

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

• ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
• ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data