Introducing Developing With Python and DynamoDB

Introducing Developing With Python and DynamoDB

Amazon Web Services is arguably one of the biggest cloud infrastructure providers in the world. Lots of organizations and companies around the globe make use of the AWS platform for their cloud services, and one of the reasons they go with AWS is because of DynamoDB and several other solutions they offer.

We know programmers love Python for a lot of solid reasons too. Python spares them the need to write loads of declarations, offers tons of data structures, and removes the need for semicolons and braces. 

Now imagine adding all of these exciting benefits with Amazon’s NoSQL key-value-supporting database – DynamoDB. In this article, you’ll find out what DynamoDB and Python Dynamodb are, including the main functionalities of Python DynamoDB, how it’s connected to AWS Python and much more.

Boto3 Resource Dynamodb

The Amazon Web Services’ Boto3 Dynamodb library is typically used to integrate Python applications with different AWS services. Clients and resources are two of the most commonly used features of Boto3 DynamoDB. 

Meanwhile, for the Boto3 Resource DynamoDB, resources mean there is a higher-level abstraction when compared to clients. Resources are generated from resource description, JSON, that is in the boto library. Besides, in Boto3 Resource Dynamodb, resources give an object-based interface when interacting with different AWS services.

What Is DynamoDB?

DynamoDB is a super fast and incredibly flexible NoSQL database service. Amazon Web Service (AWS) offers this superb database service for web apps, gaming, mobile apps, and even IoT devices. 

Amazon DynamoDB is great for developers who need to build serverless and more modern applications with the potential for global scalability while supporting millions of read and write requests every second and petabyte of data. 

What’s more, DynamoDB is built to run high-performing, internet-wide applications. Existing traditional relational databases would’ve overburdened these applications.

Main Concepts of DynamoDB

Several concepts define DynamoDB as a database service. From table to query to scan, here are some of the main concepts of DynamoDB:

  • Table: The table comes as a collection capable of holding virtually infinite numbers of unique items while allowing for secondary indices.
  • Item: As a main concept of DynamoDB, items are its most basic unit. Items hold data attributes that are structured in JSON.
  • Secondary Index: Secondary indices duplicate the table items using different sort-key and primary-key.
  • Streams: It is a constant stream of state-altering operations executed against a table. 
  • Attribute: An attribute is a key-value pair containing informational data points about a particular item in the database table. 
  • Query: It is an operation to retrieve a specific item or set of items. 
  • Sort Key: It is a special attribute form essential in organizing items in a different order of sorting. 
  • Primary Key: Primary key is another special form of the attribute used in referencing items. 
  • Filter: Filters are applicable rules you can use after executing a scan or query while doing so before results are returned to the requester. 
  • Scan: Operation to scan the whole table or a section or part of it.

What Are the Main Functionalities of DynamoDB?

DynamoDB allows users to offload the administrative workload of scaling and operating a distributed database. Users don’t have to worry about hardware provisioning, configuration & setup, replication, cluster scaling, or even software patching. Here are some of the main functionalities of DynamoDB: 

#1. Scalability

DynamoDB offers extreme scalability. Its automatic partitioning model spreads data across partitions while raising throughput as data volume grows. The user does not need to intervene for this to be possible.

#2. Three Basic Data Model

DynamoDB makes use of three basic data model units: Items, Tables, and Attributes. In simple terms, tables are a collection of items while items are a collection of attributes. Attributes are like key-value pairs and are basic units of information.

Tables on the other hand are like tables in relational databases but without the fixed schemas usually associated with them. Items are similar to rows in the RDBM table but with DynamoDB you need a primary key. AWS DynamoDB supports two types of primary keys: hash and range type primary key and hash type primary key.

#3. Predictable Performance

Amazon DynamoDB has predictable performance according to AWS. The DynamoDB should be able to deliver extremely predictable performance if Amazon’s reputation for service delivery is anything to go by. Users get to control the quality of service they get when they choose between Eventual Consistency and Strong Consistency (Read-after-Write).

More so, DynamoDB offers Provisioned Capacity. With this, users can bank at least 5 minutes of unused capacity which comes in handy during short bursts of activity.

#4. DynamoDB Index

Local Secondary Index (LSI) and Global Secondary Index (GSI) are the two types of indexes in AWS DynamoDB. You need a mandatory range key for an LSI while a GSI requires a hash range key or a hash key. What’s more, GSI’s are placed in separate tables, spanning multiple partitions and DynamoDB supports up to 5 global secondary indexes. Overall, you need to judiciously select your hash key while you create a GSI. This is because the key you choose will be used for partitioning.

#5. DynamoDB Partitions

With DynamoDB, a hash key automatically partitions data and so you will need to select a hash key when implementing a GSI. Throughput and table size are two factors that determine the partitioning logic.

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Here’s How It’s Connected To AWS Python

Python DynamoDB users require a basic understanding of NoSQL databases and solid experience with Python. In addition to that, here are some other things you need to set up DynamoDB Python:

  • DynamoDB Local: You need to download and configure DynamoDB before being able to connect and set up Python DynamoDB. 
  • Python: You need to try downloading and installing Python for python dynamodb. It is ideal to go for Python version 2.7 or later versions and you can download it from Python’s website.
  • IDE: Lastly, for your DynamoDB python, there’s a need to use a code editor that you prefer or an integrated development environment.

Steps To Connect Dynamo To Python and Set Up Python DynamoDB 

Step 1

First, to set up Dynamodb Python, you need to download and install the Java SE. You need at least version 8.x or a much newer version of Java Runtime Environment (JRE) to run DynamoDB on your computer. It doesn’t run on the earlier versions of JRE.

 Step 2

Download and install the AWS CLI Installer. After that, type this command aws –version in the command prompt to verify your installation. Getting an error means you may need to add a Path variable.

Step 3

The next step to setting up Python DynamoDB is downloading and extracting the Amazon DynamoDB. After this, go to the folder where you made the extraction and type this command java -Djava.library.path=./DynamoDBLocal_lib -jar DynamoDBLocal.jar -sharedDb in a command prompt.

Step 4

Next, try to configure credentials by typing in the aws configure command in a new command prompt.

Step 5

After the above step, type in this aws dynamodb list-tables –endpoint-url http://localhost:8000 as the next command. Doing so should bring back an empty list of tables if you don’t already give existing ones. 

Here’s How You Can Alternatively Setup Amazon DynamoDB As a Web Service

To connect to your DB via Python, you need to first set up and activate a virtual environment with this command:

/* Install virtual environment */

pip install virtualenv

/* Create a virtual environment */

python -m virtualenv venv 

/* If the above doesn’t work, try the following */

python -m venv venv

/* Activate the virtual environment */

venv/Scripts/activate

Next, use the boto3 module, so you interact with the DynamoDB’s local instance

pip install boto3

After that, you will have to import the library. In addition to this, you also need to create a database object with this: import boto3

Next, create a class and add the CRUD operations as the methods of the class.

class dtable:

    db = None

    tableName = None

    table = None

    table_created = False

def __init__(self):

    self.db  = boto3.resource(‘dynamodb’,

    endpoint_url=”http://localhost:8000″)

    print(“Initialized”)

You will need to test your code by creating an instance of the class

if __name__ == ‘__main__’:

  movies = table()

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Conclusion

Python DynamoDB is one of the best choices for web apps, mobile apps, gaming, and IoT devices. What’s more, Python has excellent support for DynamoDB. Amazon’s DynamoDB is great at providing NoSQL services with a solid, consistent, and predictable performance. 

It saves users from the difficulties encountered when they use a distributed system. If you’re looking to build your next big internet-based scale application, Python DynamoDB, along with a strong ecosystem, will be massive.

The Best Guide to Build Data Pipeline in Python

Guide to Build Data Pipeline in Python

The Best Guide to Build Data Pipeline in Python

Data is constantly evolving thanks to cheap and accessible storage. As such, intelligent clients build their business systems to take and process more data.

Afterward, they load the results in a storage repository (data lake) to keep them safe and ready for analysis. Individuals use this python data pipeline framework to create a flexible and scalable database.

A functional data pipeline python helps users process data in real-time, make changes without data loss, and allow other data scientists to explore the data easily. In this post, you will discover the right tools and methods of building data pipelines in Python. 

Python Data Pipeline Framework

A data pipeline python framework is similar to a data processing sequence that uses the Python programming language. Usually, data that is yet to be on the centralized database is processed at the beginning of Python pipelining. 

Then there will be a sequence of stages, where every step now produces an output that becomes the input for the following location. This will be in a continuous flow till the pipeline is finished. 

However, autonomous steps may be conducted simultaneously in certain instances. Every python data pipeline framework contains three major components:

  • Source
  • Processing step (or steps)
  • Destination (sink or Data Lake)

Here is how it works: the framework allows data to move from a source application to a sink (data warehouse). Depending on the type of application, the flow may also continue from a data lake to storage for analysis or directly to a payment processing system. 

Some frameworks are built with a similar sink and source, allowing programmers to focus on modification or processing steps. As such, Python pipelining deals mainly with data processing between two points. 

It is important to note that more processing steps can exist between these two points. 

Data created by a single source website or process may feed several data pipeline python programming. Those streams may be reliant on the results of several other applications or pipelines.

For instance, let’s take comments made by several Facebook users on the social media app. 

Such comments might create data for a real-time analysis that tracks social media comments. The analysis, which is from a source, can be used on a sentiment assessment application that reveals a good, unfavorable, or neutral outcome. Alternatively, it can be used on an app that plots each comment on a globe map. 

The application is different even though the data is similar. Each of these apps is based on its own set of python data pipeline frameworks that must run efficiently before the user sees the outcome. 

Data processing, augmenting, refinement, screening, grouping, aggregation, and analytics application to that data are all common phrases in data pipeline python. One major type of data pipeline utilized by programmers is ETL (Extract, Transform, Load). ETL, which works using the python framework, simplifies the process of data pipelining. 

The first step in the python ETL pipeline is extraction, i.e., getting data from the source. Then, the data is processed in the second stage, known as Transform. The final stage of the data pipeline framework is Load which involves putting the data to the last point.

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Necessary Python Tools and Frameworks for Data Pipeline 

Python is a sleek, flexible language with a vast environment of modules and code libraries. Understanding the required frameworks and libraries, like workflow management tools, helps draft data in data pipelines. 

Developers use support tools and libraries used for accessing and extracting data to write ETL in Python. 

Workflow Management

Workflow management refers to creating, altering, and tracking workflow applications that sequentially regulate the completion of corporate processes. It coordinates the maintenance and engineering of tasks in the framework of ETL. 

Workflow systems like Airflow and Luigi can also perform ETL activities.

  • Airflow: Apache Airflow utilizes directed acyclic graphs (DAG) to depict task interactions in the ETL framework. The activities carried out in a DAG include both dependencies and dependents as they are directed. 

Note that visiting any stage with this system will not make the task drift back or revisit a prior activity as they are not cyclic but acyclic. Airflow has a command-line interface (CLI) and a graphical user interface (GUI) for tracking and viewing tasks.

  • Luigi: Spotify creators implemented Luigi to handle and streamline operations. An example is the creation of weekly playlists as well as suggested mixes. 

It’s now designed to operate with a plethora of workflow systems. Intended users should note that Luigi isn’t intended to work past tens of thousands of programmed processes.

Data Movement and Processing

Python may use libraries like pandas, Beautiful Soup, and Odo to gather, modify, and transmit data in addition to overall workflow management and planning.

  • Pandas: Pandas is an analytical toolkit with powerful data manipulation, making it straightforward to use. You can use Panda for data manipulation and general data work that has links with other tasks. 

The Panda library can connect with other functions like manually designing and disseminating a machine learning approach(algorithm) within a research group. It can also build up autonomous programs that analyze information for a dynamic(real-time) dashboard. 

Developers frequently use Panda in conjunction with Scipy, sci-kit learns, and NumPy. These tools are mathematical, analytical, and statistical libraries that aid in data movement and processing.

  • Beautiful Soup: Beautiful Soup is a popular online extracting and processing tool used for gathering data. It has tools for interpreting structured datasets, like JSON records and HTML pages that can be gotten on the internet. 

Beautiful Soup enables programmers to extract data sets from even the most disorderly online applications.

  • Odo: Odo has a single, self-explanatory function that converts the data across formats.Odo (source, target) may be referred to as native Python data sets by Programmers. 

The data gathered can be instantly transformed and available for implementation by other codes in the ETL framework.

Self-Contained ETL Toolkits

Bonobo, petl, and pygrametl are among the subset of the Python libraries. These toolkits are the features that make up entirely the ETL framework.

  • Bonobo: Bonobo is a simple toolkit that executes ETL tasks using basic Python abilities like functions and iterators. These features are interlinked in DAGs and can be launched simultaneously. 

Bonobo is developed for making alterations that are straightforward, spontaneous, yet convenient to evaluate and track.

  • Petl: petl is a versatile ETL tool with an emphasis on convenience and accessibility. This program is not suitable for extensive or memory-intensive databases and pipelines, notwithstanding its flexibility. 

It’s best suited as a lightweight ETL tool that analyzes and monitors straightforward processes.

  • Pygrametl: pygrametl also delivers ETL capability in Python script that may be easily integrated into similar Python programs. Interfaces with Jython & CPython modules are included in pygrametl, enabling coders to interface with other applications while increasing ETL efficiency and productivity.

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Getting Started with Data Pipelines

Generally, website owners need data pipeline python to figure out the number of monthly or yearly users. 

Some people may need Python pipelining for weather predictions through a weather database. In the previous example, you will find that data pipelining can analyze comments on a site. 

Programmers build data pipelines using different techniques depending on several factors. Such factors may involve the type of compatible library tools, business goals, and the technical goals of the programmer. 

Python functions well when handling hierarchical database systems and dictionaries, both of which are crucial in ETL. When building data pipeline python for a web source, you will need two things:

  1. The website’s Server Side Events (SSE) to get real-time streams. Some programmers develop a script to do this while others request or purchase the web’s API. After receiving the data, they use Python’s Pandas module to analyze them in groups of 100 items and then store the outcomes into a central database or data lake.
  2. You’ll need a current version of Python downloaded to start Python pipelining. It’s typically ideal to commence a new task with a clean version of the program. 

Once the source application is ready, set up an SQLite Database using the sqlite3 C library. Note that the data obtained from the source web is now available in JSON format. You can save it to your current directory as a JSON document. 

You can also use the “dt” (DateTime) parameter to name each JSON file. Afterward, transfer the file to the dictionary before moving it to the Pandas library. 

Some programmers use the Moto library to run Python pipelining. This system simulates the  AWS (Amazon Web Services) architecture located in a host server. If you are working with Moto library, you must use SQS (Simple Queue System) to organize data from the web source. 

You will also need to use S3, a simple storage framework, as the sink (data lake). Note that S3 will store the outcomes as CSVs.

Python is flexible enough that programmers may utilize native database systems to code practically any ETL operation. For instance, using the in-built Python math package to remove null entries from a sequence is simple. Developers may also use category comprehensions to accomplish the same goal.

It is inefficient to design the complete ETL process from the start. As such, many ETL pipelining combines native Python code and well-defined procedures or classes. This includes those from the frameworks described above. 

Users may use pandas library to screen a whole DataFrame of values having nulls, for example:

filtered = data.dropna()

Many systems include Python SDKs (software development kits), APIs, and other tools, which are sometimes beneficial in ETL scripting. The Anaconda framework, for instance, is a Python compilation of data-related modules and frameworks. 

It comes with its own package management and cloud storage for programming notebooks and Python setups. Many of the information that pertains to regular Python programming is suitable for data pipeline frameworks. 

As a result, developers should adhere to several language-specific rules that make programs brief and clear while still expressing the programmer’s objectives. Visibility, an efficient runtime environment, and keeping an eye on dependencies are all essential.

Processing Data Streams With Python

A streaming data pipeline transmits data from source to destination instantaneously (in real-time), making it relevant to the data processing steps. Streaming data pipelines are used to feed data into data warehouses or disseminate to a data stream. 

The streaming data pipelines shown below are for analytical applications.

Kafka Messages to Amazon S3

The source and destination of data might rapidly emerge into a maze of intertwining streaming data pipelines. With the use of Kafka, developers can grow their application both horizontally and vertically. 

They can also manage numerous sources and destinations. Developers typically expand Kafka messages to S3 and accommodate enormous workflows.

Amazon’s Credit Card Data Protection

Amazon Kinesis is a dynamic streaming service that Amazon supports. It works well with Redshift, S3, and other analytical cloud platforms. The protection service utilizes an identified credit card type as a partitioning key in a file. 

After the partitioning, credit card types are then used for data disguising by Kinesis. It then disseminates the data to Amazon.

Tracking Twitter Mentions

A tweeter could be interested in following tweets of their beloved team on Twitter. Opinions about these teams might be employed in determining budget investment in advertisements. This attitude analysis pipeline allows developers to transfer data from Twitter to Apache Kafka. 

After this task, it then prepares the data for the Azure Sentiment Analysis before it is ultimately stored for accessibility in developers’ free time.

Tensorflow’s Machine Learning

Machine learning gathers insights from enormous, unstructured data sets by applying algorithms to them. For instance, data on breast cancer tumors might be analyzed and categorized as life-threatening or non-deadly. 

Programmers do this to fully understand the therapy and preventive measures in the environment and society. This data pipeline demonstrates how to use Tensorflow to consume data and generate predictions or classifications instantaneously.

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Conclusion

All this in-depth information should be enough for interested developers to start on their python data pipeline framework. Most of the tutorials here can be used to practice and learn Python pipelining from small real-time data. 

However, a massive data pipeline requires careful planning and design for easy handling. Developers now need a high-level abstraction of how the data should move from source forms to the respective destination.