How To Master Data Structures & Algorithms (Study Strategies)

In this video, I’ll be giving a simple 3 part study strategy to learn data structures and algorithms.

Example Notes (not complete)
https://drive.google.com/file/d/155iZJGBjziDM1hKHJDjsdxUVpVV3YQ7i/view?usp=sharing

📒 Timestamps 📒

  • 0:00 - Intro
  • 2:01 - Take Data Structures Class
  • 3:10 - Note Taking Tips
  • 5:38 - Sponsorship
  • 6:28 - Flashcards (Spaced Repetition)
  • 7:34 - Practice (using Leetcode)

#algorithms #data-structures

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How To Master Data Structures & Algorithms (Study Strategies)
Siphiwe  Nair

Siphiwe Nair

1620466520

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

Gerhard  Brink

Gerhard Brink

1620629020

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

Shardul Bhatt

Shardul Bhatt

1622789531

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 TechnologiesCall 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

Wasswa  Meagan

Wasswa Meagan

1621986060

Basics of Data Structure Algorithms

Basics of Data Structure Algorithms

If I ask you what is your morning routine, what will you answer? Let me answer it for you. You will wake up in the morning, freshen up, you’ll go for some exercise, come back, bath, have breakfast, and then you’ll get ready for the rest of your day.

If you observe closely these are a set of rules that you follow daily to get ready for your work or classes. If you skip even one step, you will not achieve your task, which is getting ready for the day.

These steps do not contain the details like, at what time you wake up or which toothpaste did you use or did you go for a walk or to the gym, or what did you have in your breakfast. But all they do contain are some basic fundamental steps that you need to execute to perform some task. This is a very basic example of algorithms. This is an algorithm for your everyday morning.

In this article, we will be learning algorithms, their characteristics, types of algorithms, and most important the complexity of algorithms.

What are Data Structure Algorithms?

Algorithms are a finite set of rules that must be followed for problem-solving operations. Algorithms are step-by-step guides to how the execution of a process or a program is done on a machine to get the expected output.

  • Do not contain complete programs or details. They are just logical solutions to a problem.
  • Algorithms are expressible in simple language or flowchart.

Characteristics of an Algorithm in Data Structure

No one would follow any written instructions to follow a daily morning routine. Similarly, you cannot follow anything available in writing and consider it as an algorithm. To consider some instructions as an algorithm, they must have some specific characteristics :

1. Input: An algorithm, if required, should have very well-defined inputs. An algorithm can have zero or more inputs.

2. Output: Every algorithm should have one or more very well-defined outputs. Without an output, the algorithm fails to give the result of the tasks performed.

3. Unambiguous: The algorithm should be unambiguous and it should not have any confusion under any circumstances. All the sentences and steps should be clear and must have only one meaning.

4. Finiteness: The steps in the algorithm must be finite and there should be no infinite loops or steps in the algorithm. In simple words, an algorithm should always end.

5. Effectiveness: An algorithm should be simple, practically possible, and easy to understand for all users. It should be executable upon the available resources and should not contain any kind of futuristic technology or imagination.

6. Language independent: An algorithm must be in plain language so that it can be easily implemented in any computer language and yet the output should be the same as expected.

Data flow of the Algorithm in Data Structure

1. Problem: To write a solution you need to first identify the problem. The problem can be an example of the real-world for which we need to create a set of instructions to solve it.

2. Algorithm: Design a step-by-step procedure for the above problem and this procedure, after satisfying all the characteristics mentioned above, is an algorithm.

3. Input: After creating the algorithm, we need to give the required input. There can be zero or more inputs in an algorithm.

4. Processing unit: The input is now forwarded to the processing unit and this processing unit will produce the desired result according to the algorithm.

5. Output: The desired or expected output of the program according to the algorithm.

Why do we need Data Structure Algorithm?

Suppose you want to cook chole ( or chickpeas) for lunch. Now you cannot just go to the kitchen and set utensils on gas and start cooking them. You must have soaked them for at least 12 hours before cooking, then chop desired vegetables and follow many steps after that to get the delicious taste, texture, and nutrition.

This is the need for algorithms. To get desired output, you need to follow some specific set of rules. These rules do not contain details like in the above example, which masala you are using or which salt you are using, or how many chickpeas you are soaking. But all these rules contain a basic step-by-step guide for best results.

We need algorithms for the following two reasons :

1. Performance: The result should be as expected. You can break the large problems into smaller problems and solve each one of them to get the desired result. This also shows that the problem is feasible.

2. Scalability: When you have a big problem or a similar kind of smaller problem, the algorithm should work and give the desired output for both problems. In our example, no matter how many people you have for lunch the same algorithm of cooking chickpeas will work every single time if followed correctly.

Let us try to write an algorithm for our lunch problem :

1. Soak chickpeas in the night so that they are ready till the next afternoon.

2. Chop some vegetables that you like.

3. Set up a utensil on gas and saute the chopped vegetables.

4. Add water and wait for boiling.

5. Add chickpeas and wait until you get the desired texture.

6. Chickpeas are now ready for your lunch.

The real-world example that we just discussed is a very close example of the algorithm. You cannot just start with step 3 and start cooking. You will not get the desired result. To get the desired result, you need to follow the specific order of rules. Also, each instruction should be clear in an algorithm as we can see in the above example.

#algorithms in data structure #data structure algorithms #algorithms

Cyrus  Kreiger

Cyrus Kreiger

1617959340

4 Tips To Become A Successful Entry-Level Data Analyst

Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.

If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?

Let’s take a look at the most important things you need to know.

#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company