In the era of big data, the ability to analyze streaming data in real-time is essential for gaining timely insights and making data-driven decisions. Amazon Managed Streaming for Apache Kafka (MSK) is a powerful solution for this purpose. This article guides you through setting up Amazon MSK, ingesting data from Twitter, and analyzing it using Apache Flink.

Amazon Managed Streaming for Apache Kafka is a fully managed, highly available, and secure Apache Kafka service to process streaming data.

BigData Analysis Using Azure Databricks

Databricks is an industry-leading modern Cloud Data Platform used for processing and transforming massive quantities of data and exploring the data through machine learning models.

In today's fast-paced digital landscape, processing and analyzing real-time data streams is crucial for timely decision-making. Amazon Kinesis, a robust data streaming service, facilitates this by collecting, processing, and analyzing streaming data. This article demonstrates how to set up and utilize Amazon Kinesis for real-time analytics using Twitter as a data source.

Amazon Kinesis services helps to collect, process, and analyze data streams in real time.

In this article we will perform data loading & data analysis using Snowflake cloud data warehouse.

Snowflake, a revolutionary cloud data warehouse that has gained immense popularity for its unique architecture and powerful features.

In this article we are going to query Amazon Redshift for Data Analytics and perform Data Visualization using Metabase.

We can submit jobs and interact directly with the data frameworks that is installed in the Google Dataproc cluster. Alternatively, we can submit one or more Job steps or Workflow Job Template to a Google Dataproc cluster. Each step is a unit of work that contains instructions to manipulate data for processing by the data framework installed on the cluster.

Google Cloud Dataproc lets us provision Apache Hadoop clusters and connect to underlying analytic data stores. With Cloud Dataproc we can set up & launch a cluster to process and analyze data with various big data frameworks very easily.

Dataproc is a fully managed and highly scalable service for running Apache Spark, Apache Flink, Presto, and 30+ open source tools and frameworks.

In this article we are going to setup Google BigQuery for Data Analytics as well as Google Data Studio for Visualization.

BigQuery is Google's fully-managed, petabyte scale, low-cost enterprise data warehouse to manage and analyze large amount of data with built-in features like machine learning, geospatial analysis, and business intelligence.

Amazon Redshift is a fast, scalable data warehouse that makes it simple and cost-effective to analyze data across data warehouse and data lake. With a few clicks, we can create a Amazon Redshift cluster in minutes.

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the AWS cloud to efficiently analyze all your data using your existing business intelligence tools.

We can submit jobs and interact directly with the data frameworks that is installed in the Amazon EMR cluster. Alternatively, we can submit one or more ordered steps to an Amazon EMR cluster. Each step is a unit of work that contains instructions to manipulate data for processing by the data framework installed on the cluster.

With Amazon EMR we can set up & launch a cluster to process and analyze data with various big data frameworks very easily.

Amazon EMR (Elastic MapReduce), is a managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark, on AWS to process and analyze vast amounts of data.

QuickSight is Amazon’s Business Intelligence pay-per-session service which allows you to create and publish interactive dashboards and charts. Quicksight can query data with Athena to provide easy-to-understand insights.