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Every enterprise applications and web applications tend to generate a huge volume of data, but what does this imply? This is one major question the data scientists and analysts are meant to answer. There is no doubt that this data is the most valuable asset of any business in the current data-centric business management approach. Making sense of these data and creating actionable insights, and making a decision accordingly is very important.

As data keeps on growing day by day, creating insights and turning them into successful decisions is the critical task for any organization to succeed. The desired data analytics pipelines need to be scalable and sturdy to adapt to this high change rate. Thus, it is also essential to set up an appropriate pipeline on the cloud, which makes sense in terms of cost reduction and flexibility. Here in this article, we will try to demystify the facts related to building scalable and flexible data pipelines on the cloud.

Various steps in creating a winning data analytics pipeline

To build an analytics pipeline, you need to first ingest the data from various sources. Next, the data needed to be processed and enriched so that the downstream applications can use the data in the desired format. Next, you should effectively store data into the data warehouse or data lake for analysis and reporting. These data can be analyzed by feeding them to powerful analytical tools. Users can apply machine learning for the creation of reports and predictions of trends. Let us explore these steps in detail.

Capturing data

Based on where data comes from, there are various options to ingest it. You may use various data migration tools for migrating data from on-premises to the cloud. Here, Google Cloud offers very strong storage transfer services. To ingest data from the third-party SaaS services, you can use APIs and send the data to warehouses. Considering Google Cloud BigQuery, a serverless data warehouse offers data transfer services that allow the users to incorporate data from various SaaS applications like Google Ads, YouTube, Teradata, Amazon S3, and ResShift.

You can also stream data live from the applications with the Pub/Sub services. You can also configure the data sources to push event messages to Pub/Sub from the sources where the subscribers tend to pick up the messages and takes appropriate actions. For example, if an IoT device can stream data using the cloud and supports MQTT protocol for IoT devices, you can also send this data to Pub/Sub. Some tools which help you do it on Google Cloud are.

  • Dataproc is fundamentally a managed Hadoop. If you use the Hadoop ecosystem, the setup may be complicated, which may take many days. However, Dataproc can easily spin up a data cluster in just 90 seconds, with which you can analyze the data instantly and accurately.
  • Dataprep is a very intelligent GUI tool that can help analyst’s process data easily and quickly without writing any codes.
  • Dataflow is a data process service, which is serverless to batch and stream data. It is built on open-source Apache Beam SDK, which lets the pipelines to be portable. The service also helps separate the storage from computing by enabling seamless scalability.

You may consult with remote DBA providers like RemoteDBA to find the most appropriate data management tools for your project.

Storing data

Once the data is processed, you have further stored it in the data warehouse or data lake for archiving or for analysis. There are different Google Cloud tools to help you with it.

  • Google Cloud Storage is the space that helps you to store videos, images, and other types of files, which is available in four storage options.
  1. Standard Storage: This is ideal for the hot data which is frequently accessed and includes websites, video streaming, and mobile applications.
  1. Nearline Storage: This is the low-cost storage option, which is ideal for data stored for a minimum of 30 days. This includes an option for data backup and also long-tail multimedia content.
  1. Coldline Storage: Another ultra-low-cost option that is ideal for data to be stored for a minimum of 90 days, which also includes a disaster recovery option.
  1. Archive Storage: This is the lowest in terms of cost. It is ideal for data to be stored for a minimum of one year by including the regulatory archives.
  • BigQuery is an option for serverless data warehousing, which can scale seamlessly to huge volumes of data in petabytes without the need to manage or maintain any physical servers. You can easily store and query the data using SQL at BigQuery. You can also easily share data and queries. There are also hundreds of free datasets for the public which can be used for analysis. It also offers connectors built-in to be ingested easily from other services and use for visualization or further processing.

Data analysis

Once data processing and storage is done on the warehouse or data lake, you can proceed with the analysis. If you are using solutions like BigQuery for data storage, you can analyze the same on BigQuery itself using SQL. If you use Google Cloud Storage, you can move the data easily to BigQuery for analysis. BigQuery features machine learning capabilities also on BigQueryML, with which you can create models.

Once the data is in the warehouse, you can use it to get insights and make predictions with machine learning. You can use tools like the Tensorflow framework and AI platform based on the needs for further processing.

  • Tensorflow is a machine learning platform that is open source, coming with many libraries, tools, and community resources.

Finally, there are many tools for data visualization also. Firstly, BigQuery itself can seamlessly connect with the visualization tools and create charts on its own. Google Cloud also offers tools for visualization. Data Studio is a free tool, which can connect to BigQuery and many other visualization services. Looker is another enterprise-grade data visualization platform, which features many business intelligence and embedded analytics tools.