The present and future of every industry sector somehow depends on the ability to use the massive amounts of data. Use the data available to drive better product quality at a lower cost. Make favourable business decisions with data. Primarily, for decades, to store a wide variety of massive data and perform analysis on it, using Data Warehouse solutions.
Traditional data warehouses designed on-premise specifically for analysis purposes. But they have a few limitations and shortcomings. That is why, the market demands cloud data warehouse solutions. However, this does not mean that traditional data warehouse ideas are dead. Classical data warehouse theory stimulates most of what modern cloud-based data warehouses do. In this blog, we will cover one of the best data warehouse solution available in the market – Google BigQuery. The problems associated with the conventional data warehouse solutions will be covered. As well how BigQuery addresses those shortcomings.
Problems with conventional Data warehouse systems
Batch data ingestion:
Traditional data warehouses are designed to perform data analytics on batch data only. They are designed for the operational reporting needs. They were designed to ingest the data mainly from ERP, CRM systems. But in today’s world, the data is coming from a lot of streaming sources. Like sensors, IOT devices, web applications through messaging systems(Kafka, RabbitMq) etc. It results in a lot of data silos. Data-driven organisations are not able to get the most out of their data due to these silos. As a consequence, their digital transformation projects failed.
Unsuitable for predictive analysis:
In the 90’s, the data in the data warehouse was meant to be used by only a few management folks. They were supposed to run a specific set of queries for reporting purposes. In its evolution, in the near 2000 era, data warehouses started handling adhoc queries for self-serviced BI. This means the queries were not predefined but decided on the fly. Then, in early 2010, data mining came into the picture. Which started providing the answers to the questions like why my sales are down? Why are my employees resigning? But nowadays, businesses are trying to get predictive insights from the data warehouses. Businesses are now focusing on what is going to happen next in the future. They want to understand future using Artificial Intelligence and Machine-learning initiatives. Which the traditional data warehouses don’t support.
High complexity and redundancy:
To setup on-prem TDW’s, most organisations purchase hardware add-ons and tools to facilitate their data needs more quickly. This leads to a complex yet redundant architecture with several data silos. Each of which needs to be regularly updated and maintained. It leads to high costs and failure rates.
In the last few years, companies have seen an astronomical explosion in the amount of data too. Traditional data warehouses are not able to handle this much data. Adding just the number of servers is not always the best option. Moreover, considering the scaling on-demand of both compute power and storage without any downtime. It is not possible in TDW.
If concluded, the traditional data warehouses no longer fit into the dynamic needs of businesses. Which clearly creates a vacuum in this space. Google BigQuery is definitely filling up this vacuum.
What is BigQuery
BigQuery is a fully managed, Serverless, highly scalable, and cost-effective cloud data warehouse designed for business agility. It is Google Cloud Platform’s enterprise data warehouse for analytics which can easily store exabytes of data. It comes with a built-in query engine which is capable of running SQL queries on terabytes of data in a matter of seconds and petabytes in minutes.We get this performance without having to manage any infrastructure.
How BigQuery is addressing the old data warehouse problems.
Both Batch and streaming data ingestion:
BigQuery’s high-speed provides streaming insertion API. It is a powerful foundation for real-time analytics, making the latest business data immediately available for analysis. We can even leverage other Google cloud services like Pub/Sub and Dataflow to stream data into BigQuery. BigQuery has the capacity to store 100,000 rows per second of streaming data. For batch we can read the TB of data per second.
Supports AI and ML:
Big query lays the foundation of Artificial Intelligence. It brings ML to the data with BigQuery ML. BigQuery ML is a Machine learning in simple SQL. Thou, can also be integrated with the AI Platform Prediction and TensorFlow. It enables to train powerful models on structured data in minutes with just SQL which helps in predictive analysis.
Google BigQuery is a serverless and fully managed service. So, it eliminates the maintenance, version upgrades and other related efforts. Google does all resource provisioning behind the scenes. So, only focus on the data and analysis. it relegates upgrading, securing, or managing the infrastructure task.
Google BigQuery is highly-highly scalable. It scales internally and can scan TB in seconds and PB in minutes.
Pay as use:
BigQuery’s default pricing model is pay as use. It is based on on-demand pricing model. This pricing model lets you pay only for the storage and compute, that you use. You only pay for the number of bytes that your query is processing. It has a built-in caching mechanism so don’t have to pay for back to back firing the same query. When a query is fired first time, the results will be cached. The same result will be displayed lightning fast for the second firing without pay anything.
Users can be assigned to various roles and groups using the Google IAM service. It is based upon their clearance level to assign read-write access, running jobs, etc permissions in a project. Google provides controls needed to secure BigQuery resources.
So, these are some of the shortcomings which BigQuery is addressing. BigQuery came along with extended functionality which is modernizing the data-warehousing.