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At a time where information and insights from data are the most significant assets that any business has, implementing data warehouse solutions has been more critical than it has been at any other time in the past. So, what exactly does a data warehouse mean? In a nutshell, a Data Warehouse is a basic data set for supporting data analysis and acting as a channel across the different sets of analytical tools and data stores. At its core, Data Warehousing solutions incorporate very versatile features that cater to varied scopes for data analysis, management, and consolidation. Not just that, you can even extrapolate crucial business data points to ensure consistency across your analytics platforms. Modern data warehouse solutions are now even coming up with inbuilt AI and ML algorithms that can tremendously help you in making key business decisions. As of today, almost all the major data warehouse solutions are delivered through the cloud with the flexibility of adding/removing features, scale-up/down within a few seconds with just a click of a mouse. Now let’s check which cloud data warehousing services providers are the best as of today. Teradata Integrated Data Warehouse: Teradata has been a market leader when comes to data warehousing and data management for more than 35 years. Teradata data warehouse is built upon the most impressive database technologies and has been serving the most leading organizations of the world. Teradata offers a 360-degree understanding and insights of the data, that can be pulled together from a range of sources. Teradata QueryGrid provides insights into actionable big data. In addition, you can deploy Teradata on IntelliCloud which is also provided by Teradata, on-premise or public, private, or hybrid cloud setup. SAP Data Warehouse Cloud SAP is very popular in the world of data analysis, data development, and business analytics. The SAP data warehouse cloud is ideal for organizations that need to make more insightful business choices. This enterprise-ready data warehouse merges all the possible data sources into one single environment that helps to get more insights from your data that can help amplify crucial business decisions. SAP’s data warehousing semantic layer helps with making analytics easier for clients with persona-driven insightful data information. It also provides instant access to application data with its pre-built adapters. Even better, SAP information warehousing is versatile, adaptable, elastic, and open, settling on it is a decent decision for organizations, all things considered. Oracle Autonomous Warehouse The Oracle Autonomous Data Warehouse offers organizations a simple to-utilize and available framework that scales with user activities. It was designed to provide super-fast, reliable, and elastic performance with minimal to zero administration. Oracle is a great product for novices and beginners who are trying to balance out the pros and cons of data warehouses on the cloud. It is the best choice for an end-to-end fully managed and reliable cloud service that makes using and implementing cloud services a walk in a park. Furthermore, Oracle’s data warehouse is exceptionally flexible, highly elastic, allowing organizations to expand their computing capabilities limits as their organization’s requirements change. You just need to pay for what you use, and everything seamlessly integrates with a range of business analytics and IoT tools. Microsoft Azure Synapse Microsoft Azure Synapse is evolved from Microsoft Azure SQL Data warehouse offerings. Synapse is the most advanced enterprise data warehousing solution that Microsoft has come up with to date. With Microsoft’s data warehouse cloud offering, you can easily query data according to individual requirements. There’s flexibility to access both provisioned and serverless on-demand resources as well. Also, Synapse empowers to leverage the power of AI, ML, and business intelligence as a part of the combined business intelligence solution ecosystem. Additionally, Microsoft has the most advanced privacy and security features across its data warehousing solutions. IBM Db2 Warehouse The IBM Db2 Warehouse provides a great relational database solution that delivers high performance and high-quality analytics to its customer. IBM Db2 seamlessly integrates with the in-memory columnar database technology from IBM. This provides an enormous advantage for organizations requiring a high-performance database solution. Users can quickly initiate the cloud deployment on the IBM cloud. There’s also a traditional on-premise version of the Db2 data warehouse solution. Google BigQuery Google BigQuery is a major component of the Google cloud ecosystem. This exceptionally adaptable and serverless cloud data warehouse solution is ideal for organizations that need to minimize expenses and at the same time benefit from the power of cloud computing. If you need to make quick business crucial decisions using data analytics BigQuery has you covered. BigQuery separates itself by its availability and accessibility. Moreover, you can proficiently run your analytics environments with a three-year TCO that is up to 34% less expensive than other cloud offerings. Integrating with AI and ML tools of Google is another key differentiator in case you’re keen on venturing into the world of AI/ML that Google BigQuery has to offer. Snowflake Snowflake is a very popular data warehousing solution that offers an assortment of choices for public cloud technology. With Snowflake, you can make your business more information-driven, empowering you to create stunning user experiences. The convenient and flexible pricing model from Snowflake helps you to save on costs and pay only for resources and services that you use. Snowflake’s very robust data warehouse architecture improves dataflow and while reducing unnecessary complexities of your data model. You additionally get self-administration admittance to all the additional usefulness that you need. Amazon Redshift Amazon Redshift is quite undoubtedly the most well-known data warehouse solution available in the market today. The service drives the analytical initiatives of new businesses and startups and Fortune 500 organizations at the same time. The biggest, greatest brands utilizing Redshift today are Intuit, Lyft, Yelp, and surprisingly Mcdonald’s. Probably the best thing about Redshift is that it integrates seamlessly with the data lake and AWS environment. Redshift allows technical users and business users to query and analyze the immense amount of non-structured, semi structured, and fully structured from a host

Handling IoT devices and doing computations on their readings is always a tedious process as it requires both Hardware and Software handling expertise. Furthermore, It becomes even more complex when you need to transfer data using traditional MQTT protocols and design your own servers and infrastructure for handling the flow of data from IoT devices to your software platform. But, what if you can leave the entire infrastructure and data flow on someone else, while just initializing all the operations by yourself, and post that sit back and relax! Does this sound interesting? Obviously Yes! AWS cloud platform provides a wide variety of services where you can setup your entire data flow of IoT devices on cloud and all the security, data backup is managed by AWS itself AWS provides different ways to setup IoT data streaming in your software. One of the ways is explained below: EDGE → API GATEWAY →  KINESIS DATASTREAMS → FIREHOSE → S3 →  ATHENA AWS Kinesis Data Streams: Kinesis Data Streams can be used for streaming real time sensor data in your dashboards. AWS Cognito: For validating the source of data. AWS API Gateway: Serverless API for injecting the data in Kinesis Data Streams. This is the endpoint, which the IoT Edge Client uses for inserting data in your data pipeline. AWS Kinesis Firehose: For dumping data obtained from Kinesis Data Streams in S3. AWS Athena: For performing different aggregation and analysis on real time data stored in S3. Challenges for Developer Although this pipeline seems to be simple and straightforward, there are certain areas where the Developer might be challenged and will have put in an extra effort for setting up the flow: Challenge: Kinesis Data Streams are designed to send a blob of data to Firehose, which in turn sends this data to its destination. But what if there is an array of records which need to be sent in each iteration? At this point, one faces the challenge, as Kinesis can easily send a single record but when it comes to multiple records, you may experience serialization errors. Solution: The solution to this problem is to use the right Kinesis method while sending data through API Gateway, PUT RECORD for sending a single record and PUT RECORDS for sending multiple records. However, you need to be cautious while designing the message templates in API Gateway for both methodsSending Data from API Gateway to Kinesis Datastreams: Challenge: Kinesis Firehose offers different formats which can be used by a Developer while creating data dumps in S3. The available data formats are CSV, JSON, Parquest, ORC. Initially you might wonder how can a data format pose to be a challenge? However, once the data size increases exponentially with time, it will become a challenge. CSV and JSON formats are very bulky data sets in the long run. Solution: The right data structure is Parquet, because all other data formats store the data in a row format while Parquet stores it in a columnar format, due to which the query execution from Athena is faster. Also, Parquet data format itself removes unnecessary space and black fields while storing, which ends up saving S3 space as well.Dumping Data in Kinesis Firehose Destination in correct format: Querying Partitioned Data from S3 in Athena: Challenge: Firehose offers a special reward of storing data in partitioned format in S3, for every date. This is a big gift when you are querying on big S3 data sets from Athena. Because each time you query S3 data from Athena, athena scans a chunk of data in S3.Two use cases may arise in this case: If the data is not partitioned: If the data is not partitioned, Athena will scan the entire chunk of data present in S3 bucket, i.e, if you have 10 years data records in S3 bucket and each time you query to fetch latest record, it will scan the entire 10 year data, resulting in lot of GET requests which in turn increases S3 cost by big numbers. If the data is partitioned: If the data is partitioned, Athena will scan only the latest day record even if you have 10 years data present inside your S3 bucket, and your GET requests won’t increase. The challenge here is; How to read partitioned data in Athena? Solution: The solution here is to use the concept of Partition Projection while reading partitioned data in S3. You can implement Partition Projection while creating Glue Tables for Firehose. Partition Projection helps reading data for a particular timestamp value only, due to which you might end up scanning only a particular set of records which are actually required while executing the query. Project Case Study: We recently created an IOT Based Product for one of the Power Sector Client. The Product focuses on receiving data from IOT sensors installed at Edge Location and then displaying this information on Web Based Application. Data from IOT sensors travels in cloud(aws) and is encrypted so that the data integrity and security is ensured.The Backend Infrastructure is designed using Amazon Web Services(AWS) and Django Rest Framework. We have used Kinesis for Real Time Streaming on our dashboard and have used Athena for displaying aggregated results for time based filters. Initially during setting up the infrastructure, we even faced some bottlenecks in AWS Kinesis Data pipelines, i.e, while reading long array jsons in Kinesis or while querying through the partitioned data stored in S3 Bucket. But with some proper research work and collaborative engineering we achieve our  goal and completed this beautiful Product. Every new recipe may not be perfect in the first go but if we take lessons from other Chef experiences then we may definitely end up making a yummy dish.!