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Cloud and Big Data
Accessing Redshift using Python and PySpark
Redshift is a cloud-based data warehousing service provided by Amazon Web Services, while Pandas is a popular data analysis library for the Python programming language, and PySpark is a powerful data processing engine that can handle large-scale data processing tasks. In this article, we will explore how to use Pandas and PySpark to read data from Redshift, enabling us to process and analyze large datasets efficiently. Further, we will also explore how to write data to a Redshift sandbox. Read from Redshift using Python Pandas To read Redshift data using the redshift-data package and pandas, you can follow these steps: # Install and import the redshift-data package and pandas pip install redshift-data pandas import redshift_data import pandas as pd # Set up the connection to your Redshift database conn = redshift_data.connect(database='your_database_name', user='your_username', password='your_password', host='your_redshift_host', port=your_redshift_port) # Execute a SQL query and read the results into a pandas dataframe query = "SELECT * FROM your_table_name" df = pd.read_sql(query, conn) # Filter the data using the df.loc[] method filtered_df = df.loc[df['column_name'] == 'value'] # Close the connection to your Redshift database conn.close() By following these steps, you can read Redshift data using the redshift-data package and pandas, and then manipulate the data as needed using pandas functions and methods. Read from Redshift using PySpark To read Redshift data into a PySpark dataframe using the redshift-data library, you can follow these steps: # install the redshift-data package pip install redshift-data # import the necessary packages from pyspark.sql import SparkSession import os import boto3 from redshift_data import RedshiftData # Set up the Spark session: spark = SparkSession.builder.appName("RedshiftData").getOrCreate() # Create a RedshiftData object and set up the credentials client = RedshiftData(region_name='us-west-2') client.set_credentials(secret_id='your_secret_id', database='your_database_name', cluster_identifier='your_cluster_identifier') # Execute the SQL query sql = 'SELECT * FROM your_table_name' response = client.execute_statement(sql) # Get the results of the query and convert it to a Pandas dataframe column_names = [column_metadata.name for column_metadata in response.column_metadata] rows = response.fetchall() result_df = pd.DataFrame(rows, columns=column_names) # Create a Spark dataframe from the Pandas dataframe pandas_rdd = spark.sparkContext.parallelize(result_df.to_dict('records')) df = spark.createDataFrame(pandas_rdd) Note: you need to import the pandas package for this to work, and you may need to adjust the code based on your specific Redshift setup. Write to Redshift using Python Pandas To write a Pandas dataframe into a Redshift sandbox using redshift-data, you can follow these steps: # Install the required packages in your Python environment pip install pandas redshift-data # Import the required packages in your Python script import pandas as pd from redshift_data import RedshiftData # Create an instance of the RedshiftData class and connect to your Redshift sandbox by passing in your database credentials rs_data = RedshiftData( user='your_username', password='your_password', host='your_redshift_host', database='your_database_name', port='your_redshift_port' ) # Create a table in your Redshift sandbox with the same structure as your Pandas dataframe. rs_data.execute(""" CREATE TABLE your_table_name ( column1 datatype1, column2 datatype2, ... ) """) # Use the pd.DataFrame.to_csv() method to convert your Pandas dataframe to a CSV file df.to_csv('your_dataframe.csv', index=False) # Use the rs_data.load() method to load the CSV file into your Redshift sandbox rs_data.load( 'your_table_name', 'your_dataframe.csv', delimiter=',', ignore_header=1 ) # Delete the CSV file using the os.remove() function import os os.remove('your_dataframe.csv') By following these steps, you can write a Pandas dataframe into a Redshift sandbox using redshift-data, enabling you to store and analyze your data in a scalable, cost-effective way. In conclusion, using Pandas and PySpark to read data from Redshift is a powerful way to handle large-scale data processing and analysis tasks. With these tools, we can efficiently manipulate and analyze large datasets, making it possible to derive insights that were previously out of reach. Whether you’re working on a data science project or managing a large-scale data processing pipeline, leveraging these tools can help you streamline your workflows and unlock new possibilities for data analysis. Comments welcome!
Cloud and Big Data
· 2023-05-06
Important AWS Services that you need to Know Now
Introduction Amazon Web Services (AWS) is a cloud-based platform that provides a wide range of infrastructure, platform, and software services. It was launched in 2006 and has since become one of the most popular cloud computing platforms in the world, used by individuals, small businesses, and large enterprises alike. AWS is known for its flexibility, scalability, and cost-effectiveness, allowing businesses to pay only for the services they use and scale up or down as needed. Its reliability and security features also make it a popular choice for businesses that need to store and process sensitive data. AWS provides a wide range of services, including compute, storage, databases, analytics, machine learning, Internet of Things (IoT), security, and more. It also offers a variety of deployment models, including public, private, and hybrid clouds, as well as edge computing services that allow computing to be performed closer to the source of data. Key services Compute services, such as Amazon Elastic Compute Cloud (EC2) and AWS Lambda Storage services, such as Amazon Simple Storage Service (S3) and Elastic Block Store (EBS) Database services, such as Amazon Relational Database Service (RDS) and DynamoDB Networking services, such as Amazon Virtual Private Cloud (VPC) and Elastic Load Balancing (ELB) Management and monitoring services, such as AWS CloudFormation and AWS CloudWatch Security and compliance services, such as AWS Identity and Access Management (IAM) and AWS Key Management Service (KMS) Analytics and machine learning services, such as Amazon SageMaker and Amazon Redshift Users can interact with these services using SSH (Secure Shell) and AWS CLI (Command Line Interface) boto3 (a Python library used to interact with AWS resources) Deep dive into services EC2 Amazon Elastic Compute Cloud (EC2) is a web service that provides resizable compute capacity in the cloud. EC2 allows users to launch and manage virtual machines, called instances, in the AWS cloud. With EC2, users can select from a variety of instance types optimized for different use cases, and can scale up or down their compute resources as needed. EC2 also allows users to choose from a range of operating systems, including Amazon Linux, Ubuntu, Windows, and others. EC2 instances can be used to run a wide range of applications, from simple web servers to complex, multi-tier applications. SSH (Secure Shell) is used to establish a secure, encrypted connection with the EC2 instance, allowing you to remotely log in and execute commands on the server. The basic syntax for the SSH command is ssh [username]@[EC2 instance public DNS] You will need to replace [username] with the username you created when setting up your EC2 instance, and [EC2 instance public DNS] with the public DNS address for your instance. Once you have established an SSH connection, you can execute commands on the EC2 instance just as you would on a local machine. Lambda AWS Lambda is a serverless compute service provided by Amazon Web Services. With Lambda, you can run your code in response to various events such as changes to data in an Amazon S3 bucket or an update to a DynamoDB table. You upload your code to Lambda, and it takes care of everything required to run and scale your code with high availability. AWS Lambda is an event-driven service, meaning it only runs when an event triggers it. AWS Lambda console or AWS Command Line Interface (CLI) can be used to create, configure, and deploy your Lambda functions. Following commands allow you to perform common tasks such as creating, updating, invoking, and deleting your Lambda functions using the AWS CLI. aws lambda create-function: Creates a new Lambda function. aws lambda update-function-code: Uploads new code to an existing Lambda function. aws lambda invoke: Invokes a Lambda function. aws lambda delete-function: Deletes a Lambda function. Boto3 can also be used to create a new Lambda function. Following code uses the boto3.client() method to create a client object for interacting with AWS Lambda. It then defines the code for the Lambda function, which is stored in a ZIP file. Finally, it uses the lambda_client.create_function() method to create the Lambda function, specifying the function name, runtime, IAM role, handler, and code. The response from this method call contains information about the newly created function. import boto3 # create a client object to interact with AWS Lambda lambda_client = boto3.client('lambda') # define the Lambda function's code code = {'ZipFile': open('lambda_function.zip', 'rb').read()} # create the Lambda function response = lambda_client.create_function( FunctionName='my-function', Runtime='python3.8', Role='arn:aws:iam::123456789012:role/lambda-role', Handler='lambda_function.handler', Code=code ) print(response) S3 (Simple Storage Service) Amazon S3 (Simple Storage Service) is a cloud storage service offered by Amazon Web Services (AWS). It provides a simple web interface to store and retrieve data from anywhere on the internet. S3 is designed to be highly scalable, durable, and secure, making it a popular choice for data storage, backup and archival, content distribution, and many other use cases. S3 allows you to store and retrieve any amount of data at any time, from anywhere on the web. It also offers different storage classes to help optimize costs based on access frequency and retrieval times. Following AWS CLI code can be used in SSH to upload, download, and delete a file from S3: aws s3 cp /path/to/local/file s3://bucket-name/key-name #code to upload aws s3 cp s3://bucket-name/key-name /path/to/local/file #code to download aws s3 rm s3://bucket-name/key-name #code to delete a file Following boto3 code can be used to upload, download, and delete a file from S3. Note that you need to replace your-region, your-access-key, your-secret-key, your-bucket-name, your-file-name, and new-file-name with your own values. Also, make sure that you have the necessary permissions to read, write, and delete objects in your S3 bucket. import boto3 # set the S3 region and access keys s3 = boto3.resource('s3', region_name='your-region', aws_access_key_id='your-access-key', aws_secret_access_key='your-secret-key') # read a file from S3 bucket_name = 'your-bucket-name' file_name = 'your-file-name' object = s3.Object(bucket_name, file_name) file_content = object.get()['Body'].read().decode('utf-8') print(file_content) # write a file to S3 new_file_name = 'new-file-name' new_file_content = 'This is the content of the new file.' object = s3.Object(bucket_name, new_file_name) object.put(Body=new_file_content.encode('utf-8')) # delete a file from S3 object = s3.Object(bucket_name, file_name) object.delete() Here’s an example of how to use a KMS key to encrypt and decrypt data in S3 using boto3: import boto3 # Create a KMS key kms = boto3.client('kms') response = kms.create_key() kms_key_id = response['KeyMetadata']['KeyId'] # Create an S3 bucket or select an existing one s3 = boto3.resource('s3') bucket_name = 'my-s3-bucket' # Grant permissions to the KMS key key_policy = { "Version": "2012-10-17", "Id": "key-policy", "Statement": [ { "Sid": "Enable IAM User Permissions", "Effect": "Allow", "Principal": {"AWS": "arn:aws:iam::123456789012:root"}, "Action": "kms:*", "Resource": "*" }, { "Sid": "Allow use of the key", "Effect": "Allow", "Principal": {"AWS": "arn:aws:iam::123456789012:user/username"}, "Action": [ "kms:Encrypt", "kms:Decrypt", "kms:ReEncrypt*", "kms:GenerateDataKey*", "kms:DescribeKey" ], "Resource": "*" } ] } kms.put_key_policy(KeyId=kms_key_id, Policy=json.dumps(key_policy)) # Enable default encryption on the S3 bucket s3_bucket = s3.Bucket(bucket_name) s3_bucket.put_encryption( ServerSideEncryptionConfiguration={ 'Rules': [ { 'ApplyServerSideEncryptionByDefault': { 'SSEAlgorithm': 'aws:kms', 'KMSMasterKeyID': kms_key_id } } ] } ) # Upload an object to S3 with encryption s3_client = boto3.client('s3') s3_client.put_object( Bucket=bucket_name, Key='example_object', Body=b'Hello, world!', ServerSideEncryption='aws:kms', SSEKMSKeyId=kms_key_id ) # Download an object from S3 with decryption response = s3_client.get_object( Bucket=bucket_name, Key='example_object' ) body = response['Body'].read() print(body.decode()) Example code that uses the boto3 library to connect to an S3 bucket, list the contents of a folder, and download the latest file based on its modified timestamp: import boto3 from datetime import datetime # set up S3 client s3 = boto3.client('s3') # specify bucket and folder bucket_name = 'my-bucket' folder_name = 'my-folder/' # list all files in the folder response = s3.list_objects(Bucket=bucket_name, Prefix=folder_name) # sort the files by last modified time files = response['Contents'] files = sorted(files, key=lambda k: k['LastModified'], reverse=True) # download the latest file latest_file = files[0]['Key'] s3.download_file(bucket_name, latest_file, 'local_filename') EBS (Elastic Block Storage) Amazon Elastic Block Store (EBS) is a block-level storage service that provides persistent block storage volumes for use with Amazon EC2 instances. EBS volumes are highly available and reliable storage volumes that can be attached to running instances, allowing you to store persistent data separate from the instance itself. EBS volumes are designed for mission-critical systems, so they are optimized for low-latency and consistent performance. EBS also supports point-in-time snapshots, which can be used for backup and disaster recovery. Using SSH/AWS CLI to manage Elastic Block Store (EBS) volumes aws ec2 create-volume --availability-zone us-east-1a --size 10 --volume-type gp2 aws ec2 attach-volume --volume-id vol-0123456789abcdef --instance-id i-0123456789abcdef --device /dev/sdf aws ec2 detach-volume --volume-id vol-0123456789abcdef aws ec2 delete-volume --volume-id vol-0123456789abcdef Using python/boto3 to manage Elastic Block Store (EBS) volumes import boto3 # create an EC2 client object ec2 = boto3.client('ec2') # create an EBS volume response = ec2.create_volume( AvailabilityZone='us-west-2a', Encrypted=False, Size=100, VolumeType='gp2' ) print(response) # attach a volume response = ec2.attach_volume( Device='/dev/sdf', InstanceId='i-0123456789abcdef0', VolumeId='vol-0123456789abcdef0' ) print(response) # detach a volume response = ec2.detach_volume( VolumeId='vol-0123456789abcdef0' ) print(response) # delete a volume response = ec2.delete_volume( VolumeId='vol-0123456789abcdef0' ) print(response) RDS (Relational Database Service) Amazon Relational Database Service (Amazon RDS) is a managed database service offered by Amazon Web Services (AWS) that simplifies the process of setting up, operating, and scaling a relational database in the cloud. It provides cost-efficient, resizable capacity for an industry-standard relational database and manages common database administration tasks, freeing up developers to focus on applications and customers. With Amazon RDS, you can choose from several different database engines, including Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle Database, and SQL Server. To use AWS RDS with PostgreSQL, follow these steps: Log in to your AWS console and navigate to the RDS dashboard. Click the “Create database” button. Choose “Standard Create” and select “PostgreSQL” as the engine. Choose the appropriate version of PostgreSQL that you want to use. Set up the rest of the database settings, such as the instance size, storage, and security group settings. Click the “Create database” button to create your RDS instance. Once the instance is created, you can connect to it using a PostgreSQL client, such as pgAdmin or the psql command-line tool. To connect to the RDS instance using pgAdmin, follow these steps: Open pgAdmin and right-click on “Servers” in the Object Browser. Click “Create Server”. Enter a name for the server and switch to the “Connection” tab. Enter the following information: – Host: This is the endpoint for your RDS instance, which you can find in the RDS dashboard. – Port: This is the port number for your PostgreSQL instance, which is usually 5432. – Maintenance database: This is the name of the default database that you want to connect to. – Username: This is the username that you specified when you created the RDS instance. – Password: This is the password that you specified when you created the RDS instance. Click “Save” to create the server. You can now connect to the RDS instance by double-clicking on the server name in the Object Browser. DynamoDB Amazon DynamoDB is a fully-managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB lets users offload the administrative burdens of operating and scaling a distributed database so that they don’t have to worry about hardware provisioning, setup, and configuration, replication, software patching, or cluster scaling. DynamoDB is known for its high performance, ease of use, and flexibility. It supports document and key-value store models, making it suitable for a wide range of use cases, including mobile and web applications, gaming, ad tech, IoT, and more. Using SSH/AWS CLI to access DynamoDB: aws dynamodb create-table --table-name <table-name> --attribute-definitions AttributeName=<attribute-name>,AttributeType=S --key-schema AttributeName=<attribute-name>,KeyType=HASH --provisioned-throughput ReadCapacityUnits=1,WriteCapacityUnits=1 aws dynamodb put-item --table-name <table-name> --item '{"<attribute-name>": {"S": "<attribute-value>"}}' aws dynamodb get-item --table-name <table-name> --key '{"<attribute-name>": {"S": "<attribute-value>"}}' aws dynamodb delete-item --table-name <table-name> --key '{"<attribute-name>": {"S": "<attribute-value>"}}' aws dynamodb delete-table --table-name <table-name> Using boto3 to work with DynamoDB: import boto3 # create a DynamoDB resource dynamodb = boto3.resource('dynamodb') # create a table table = dynamodb.create_table( TableName='my_table', KeySchema=[ { 'AttributeName': 'id', 'KeyType': 'HASH' } ], AttributeDefinitions=[ { 'AttributeName': 'id', 'AttributeType': 'S' } ], ProvisionedThroughput={ 'ReadCapacityUnits': 5, 'WriteCapacityUnits': 5 } ) # put an item into the table table.put_item( Item={ 'id': '1', 'name': 'Alice', 'age': 30 } ) # get an item from the table response = table.get_item( Key={ 'id': '1' } ) # print the item item = response['Item'] print(item) # delete the table table.delete() Amazon Virtual Private Cloud (VPC) It is a service offered by Amazon Web Services (AWS) that enables users to launch Amazon Web Services resources into a virtual network that they define. With Amazon VPC, users can define a virtual network topology including subnets and routing tables, and control network security using firewall rules and access control lists. AWS Elastic Load Balancer (ELB) It is a managed load balancing service provided by Amazon Web Services. It automatically distributes incoming application traffic across multiple targets, such as Amazon EC2 instances, containers, and IP addresses, in one or more Availability Zones. ELB allows you to easily scale your application by increasing or decreasing the number of resources, such as EC2 instances, behind the load balancer, and it provides high availability and fault tolerance for your applications. There are three types of load balancers available in AWS: Application Load Balancer, Network Load Balancer, and Classic Load Balancer. AWS CloudFormation It is a service provided by Amazon Web Services that helps users model and set up their AWS resources. It allows users to create templates of AWS resources in a declarative way, which can then be versioned and managed like any other code. AWS CloudFormation automates the deployment and updates of the AWS resources specified in the templates. This service makes it easier to manage and maintain infrastructure as code and provides a simple way to achieve consistency and repeatability across environments. Users can define the infrastructure for their applications and services, and then AWS CloudFormation takes care of provisioning, updating, and deleting resources, based on the templates defined by the user. AWS CloudWatch It is a monitoring service provided by Amazon Web Services (AWS) that collects, processes, and stores log files and metrics from AWS resources and custom applications. With CloudWatch, users can collect and track metrics, collect and monitor log files, and set alarms. CloudWatch is designed to help users identify and troubleshoot issues, and it can be used to monitor AWS resources such as EC2 instances, RDS instances, and load balancers, as well as custom metrics and logs from any other application running on AWS. CloudWatch can also be used to gain insights into the performance and health of applications and infrastructure, and it integrates with other AWS services such as AWS Lambda, AWS Elastic Beanstalk, and AWS EC2 Container Service. AWS Key Management Service (KMS) It is a managed service that makes it easy for you to create and control the encryption keys used to encrypt your data. KMS is integrated with many other AWS services, such as Amazon S3, Amazon EBS, and Amazon RDS, allowing you to easily protect your data with encryption. With KMS, you can create, manage, and revoke encryption keys, and you can audit key usage to ensure compliance with security best practices. KMS also supports hardware security modules (HSMs) for added security. import boto3 # create a KMS client client = boto3.client('kms') # Encrypt data using a KMS key response = client.encrypt( KeyId='alias/my-key', Plaintext=b'My secret data' ) # Decrypt data using a KMS key response = client.decrypt( CiphertextBlob=response['CiphertextBlob'] ) # Manage encryption keys # Create a new KMS key response = client.create_key( Description='My encryption key', KeyUsage='ENCRYPT_DECRYPT', Origin='AWS_KMS' ) # List all KMS keys in your account response = client.list_keys() # Handle errors gracefully when using KMS import botocore.exceptions try: response = client.encrypt( KeyId='alias/my-key', Plaintext=b'My secret data' ) except botocore.exceptions.ClientError as error: print(f'An error occurred: {error}') Amazon SageMaker It is a fully-managed service that enables developers and data scientists to easily build, train, and deploy machine learning models at scale. With SageMaker, you can quickly create an end-to-end machine learning workflow that includes data preparation, model training, and deployment. The service offers a variety of built-in algorithms and frameworks, as well as the ability to bring your own custom algorithms and models. SageMaker provides a range of tools and features to help you manage your machine learning projects. You can use the built-in Jupyter notebooks to explore and visualize your data, and use SageMaker’s automatic model tuning capabilities to find the best hyperparameters for your model. The service also offers integration with other AWS services such as S3, IAM, and CloudWatch, making it easy to build and deploy machine learning models in the cloud. With SageMaker, you only pay for what you use, with no upfront costs or long-term commitments. The service is designed to scale with your needs, so you can start small and grow your machine learning projects as your data and business needs evolve. Amazon Redshift It is a cloud-based data warehousing service provided by Amazon Web Services (AWS). It is a fully managed, petabyte-scale data warehouse that enables businesses to analyze data using existing SQL-based business intelligence tools. Redshift is designed to handle large data sets and to scale up or down as needed, making it a flexible and cost-effective solution for data warehousing. Using redshift-data to connect to Redshift cluster: import boto3 # Connect to the Redshift cluster client = boto3.client('redshift-data', region_name='us-west-2') response = client.connect(Database='my_database', DbUser='my_user', DbPassword='my_password', ClusterIdentifier='my_cluster') # Execute a SQL statement response = client.execute_statement(Sql='SELECT * FROM my_table', ConnectionId=response['ConnectionId']) # Fetch the results statement_id = response['Id'] response = client.get_statement_result(Id=statement_id) # Print the results for row in response['Records']: print(row) # parse the response JSON and create a Pandas DataFrame # we parse the JSON using json.dumps and create a Pandas DataFrame from the resulting string using pd.read_json. The resulting DataFrame will have the columns and rows returned by the SQL query. df = pd.read_json(json.dumps(response['Records'])) Using psycopg2 to connect to Redshift cluster: pip install boto3 psycopg2 import boto3 client = boto3.client('redshift') # create a new Redshift cluster using boto3: response = client.create_cluster( ClusterIdentifier='my-redshift-cluster', NodeType='dc2.large', MasterUsername='myusername', MasterUserPassword='mypassword', ClusterSubnetGroupName='my-subnet-group', VpcSecurityGroupIds=['my-security-group'], ClusterParameterGroupName='default.redshift-1.0', NumberOfNodes=2, PubliclyAccessible=False, Encrypted=True, HsmClientCertificateIdentifier='my-hsm-certificate', HsmConfigurationIdentifier='my-hsm-config', Tags=[{'Key': 'Name', 'Value': 'My Redshift Cluster'}] ) print(response) # connect to the Redshift cluster and create a new database import psycopg2 conn = psycopg2.connect( host='my-redshift-cluster.xxxxxxxx.us-west-2.redshift.amazonaws.com', port=5439, dbname='mydatabase', user='myusername', password='mypassword' ) cur = conn.cursor() cur.execute('CREATE DATABASE mynewdatabase') conn.commit() cur.close() conn.close() # Query the Redshift database import psycopg2 conn = psycopg2.connect( host='my-redshift-cluster.xxxxxxxx.us-west-2.redshift.amazonaws.com', port=5439, dbname='mynewdatabase', user='myusername', password='mypassword' ) cur = conn.cursor() cur.execute('SELECT * FROM mytable') for row in cur: print(row) cur.close() conn.close() # Delete the Redshift cluster using boto3 import boto3 client = boto3.client('redshift') response = client.delete_cluster( ClusterIdentifier='my-redshift-cluster', SkipFinalClusterSnapshot=True ) print(response) Comments welcome!
Cloud and Big Data
· 2022-07-02
Important GCP Services that you need to Know Now
Introduction Google Cloud Platform (GCP) is a cloud computing platform offered by Google. GCP provides a comprehensive set of tools and services for building, deploying, and managing cloud applications. It includes services for compute, storage, networking, machine learning, analytics, and more. Some of the most commonly used GCP services include Compute Engine, Cloud Storage, BigQuery, and Kubernetes Engine. GCP is known for its powerful data analytics and machine learning capabilities. It offers a range of machine learning services that allow users to build, train, and deploy machine learning models at scale. GCP also provides powerful data analytics tools, including BigQuery, which allows users to analyze massive datasets quickly and easily. GCP is a popular choice for businesses of all sizes, from small startups to large enterprises. It offers flexible pricing options, with pay-as-you-go and monthly subscription plans available. Additionally, GCP offers a range of tools and services to help businesses optimize their cloud costs, including cost management tools and usage analytics. Some of the most commonly used GCP services are: Google Compute Engine (GCE) - a virtual machine service for running applications on the cloud. Google Kubernetes Engine (GKE) - a managed Kubernetes service for container orchestration. Google Cloud Storage (GCS) - a scalable object storage service for unstructured data. Google Cloud Bigtable - a NoSQL database service for large, mission-critical applications. Google Cloud SQL - a fully managed relational database service. Google Cloud Datastore - a NoSQL document database service for web and mobile applications. Google Cloud Pub/Sub - a messaging service for real-time data delivery and streaming. Google Cloud Dataproc - a fully managed cloud service for running Apache Hadoop and Apache Spark workloads. Google Cloud ML Engine - a managed service for training and deploying machine learning models. Google Cloud Vision API - an image analysis API that can identify objects, faces, and other visual content. Google Cloud Speech-to-Text - a speech recognition service that transcribes audio files to text. Google Cloud Text-to-Speech - a text-to-speech conversion service that creates natural-sounding speech from text input. How to access GCP services use the Cloud Client Libraries or the Cloud APIs directly. To use the Cloud Client Libraries, you’ll need to first authenticate your application. You can do this by creating a service account, downloading a JSON file containing your credentials, and setting the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of the file. Once you’ve authenticated, you can import the relevant client library and start using GCP services. use the Cloud APIs directly by making REST requests. To make requests to the Cloud APIs, you’ll need to authenticate and authorize your application by creating a service account and generating a private key. You can then use this key to sign your requests using OAuth 2.0. Once you’ve authenticated, you can make requests to the relevant API endpoints using HTTP requests. Comments welcome!
Cloud and Big Data
· 2020-09-03
Important Azure Services that you need to Know Now
Introduction Azure is a cloud computing platform and set of services offered by Microsoft. It provides a wide range of services such as virtual machines, databases, storage, and networking, among others, that users can access and use to build, deploy, and manage their applications and services. Azure also offers a variety of tools and services to help users with tasks such as data analytics, artificial intelligence, and machine learning. Azure provides a pay-as-you-go pricing model, allowing users to only pay for the services they use. Key Services Azure Virtual Machines: a cloud computing service that allows users to create and manage virtual machines in the cloud. Azure App Service: a platform as a service (PaaS) offering that allows developers to build, deploy, and scale web and mobile apps. Azure Functions: a serverless computing service that allows developers to run small pieces of code (functions) in the cloud. Azure Blob Storage: a cloud storage service that allows users to store and access large amounts of unstructured data. Azure SQL Database: a fully managed relational database service that allows users to build, deploy, and manage applications with a variety of languages and frameworks. Azure Active Directory: a cloud-based identity and access management service that provides secure access and single sign-on to various cloud applications. Azure Cosmos DB: a globally distributed, multi-model database service that allows users to manage and store large volumes of data with low latency and high availability. Azure Machine Learning: a cloud-based machine learning service that allows users to build, train, and deploy machine learning models at scale. Azure DevOps: a set of services that provides development teams with everything they need to plan, build, test, and deploy applications. Azure Kubernetes Service: a fully managed Kubernetes container orchestration service that allows users to deploy and manage containerized applications at scale. How to access the services Azure Portal: The Azure Portal is a web-based user interface that provides access to Azure services. Users can log in and manage their resources in the Azure Portal. Azure CLI: The Azure Command-Line Interface (CLI) is a cross-platform command-line tool that allows you to manage Azure resources. Azure PowerShell: Azure PowerShell is a command-line tool that allows users to manage Azure resources using Windows PowerShell. Azure SDKs: Azure provides Software Development Kits (SDKs) for various programming languages, such as .NET, Java, Python, Ruby, and Node.js. These SDKs provide libraries and tools for interacting with Azure services. REST APIs: Azure services can be accessed using REST APIs. Developers can use any programming language that supports HTTP/HTTPS to interact with Azure services. Azure Functions: Azure Functions is a serverless compute service that allows you to run code on demand. You can use Azure Functions to access Azure services. Azure Logic Apps: Azure Logic Apps is a cloud-based service that allows you to create workflows that integrate with various Azure services. Azure DevOps: Azure DevOps is a set of development tools that includes features such as source control, continuous integration, and continuous delivery. Developers can use Azure DevOps to manage and deploy their applications to Azure services. Comments welcome!
Cloud and Big Data
· 2020-08-06
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