ETL and Modern Approaches

It is the fact that more and more businesses are having easier access to data to be used in competitive advantages. Data warehouses, lowering storage costs, modernised ETL tools are enabling businesses to store, access and process data.  Data-Driven decisions and accepting data as an integral business asset has become a norm to be relevant in the competitive market landscape. This blog is an effort to highlight how businesses are putting up technologies at work to ingest data from across distributed sources.

As per Bissfully’s 2020 SaaS trends report, smaller companies (0-50 employees) use an average of 102 SaaS applications, while mid-sized ones (101-250 employees) use about 137 SaaS applications. Enterprises, meanwhile, can have as many as 288 SaaS applications in use across the entire organization!

These applications are serving important business objectives ranging from customer service automation, helpdesk tools, billing softwares, communication & collaboration tools to marketing automation apps to CRM applications. All these applications regularly log enormous rows of data accessible through APIs. Much of these data carries valuable insight about the business. It’s clear that even a small or medium-sized business can do a lot with the data at its disposal. Here we are going to take a quick look at ETL and why it’s important to powering data-driven businesses, and how the rise of modern ETL tools has changed the ETL process forever.

ETL – the first step towards Data-driven decision making

Smarter and faster decisions are always expected by any team irrespective of functions. Be it marketing team wanting better ROI on their efforts, customer service team want means and decisions to reduce their time to service, sales team looks for reduced cost of sales, while production team must be looking for optimised resource utilisation. We can safely claim that in achieving any business objective, faster and most logical decision combining experience and data driven insights can make a big difference.

ETL is the first step towards achieving a broader goal of being a data driven decision maker. For any business small or large, they ingest data from a spectrum of sources through the extracting, transforming and loading the data. Processed data is the fuel for analysts to make further analytical operations to find actionable insights to be consumed by various stakeholders/teams. It all starts with ETL in any business intelligence and data analytics solutions

A traditional ETL approach

Extraction step is about processes that handle pulling the data from various sources via application databases, files or APIs. The next step is to consolidate, clean and make it data consistent for modelling required to fit the analysis. Data cleansing and quality are also very important tasks before the data is made ready for loading into data warehouse. This forms the data transformation step, followed by Loading of cleaned tables into data warehouse for further slicing and dicing required for analysis.

Traditional ETL has a specific challenge on its scalability as the volume and business needs grows on the journey. A full-time database engineer is required to develop, maintain and keep changing scripts to keep the whole ETL process operating smoothly. Schema changes at data sources or upgrades/changes in APIs often leads to manual changes to be done on transformation scripts by engineers. Immediate actions are required to keep the ETL process up and running without downtime. In scenarios of varying type and velocity of data from a range of sources and 3rd party applications, maintenance of ETL scripts for each of changes limits its scalability and performance.

The modern ETL process

Nowadays ETL tools are delivering high load high performance. With integrations available for most SaaS applications and providers putting a dedicated team of data engineers, pressure is reduced on your in-house team. There are ETL tools available built to connect to most data warehouses permitting businesses to reduce time to ETL by plugging their apps to Data Warehouse in the cloud. Scalable ETL processes in the cloud take care of the rest.

Simple drop-down selection features for controlling data orchestration within the apps, practically removes the need to keep your own servers or EC2 box or building DAGs to run on platforms like Airflow. ETL tools can also typically offer more robust options for appending new data incrementally, or only updating new and modified rows, which can allow for more frequent loads, and closer to real-time data for the business. With this simplified process for making data available for analysis, data teams can focus on finding new applications for data to generate value for the business.

The ETL and data warehouses

Data warehouses are the present and future of data and analytics. Storage costs on data warehouses have drastically reduced in recent years, which allows businesses to load as many raw data sources as possible without the same concerns they might have had before.
Data warehouses are fundamental to the present and future of data & analytical methods. Dwindling cost of storage allows businesses to ingest a varied range of data sources without the same concerns they have had before.

BI engineers can ingest row data before applying transformation scripts, permitting them to do transformational operations in the warehouse itself without need of separate staging environments. Besides increasing accessibility of data and common data access language, SQL enables businesses to attain more flexibility in making data as core foundation for making decisions in their business advantage.

Achieving the speed and performance with modern ETL process

Traditional ETL processes faced performance and scalability limitations as the requirements grew and on-premise data warehouse was backed with hardware additions to a certain limit considering the cost and values. Businesses needed to transform themselves to adapt to cloue era to make sure BI solutions keep serving the purposes without hassle.

The modern ETL process in today’s data warehouses sidesteps this issue by offloading the compute resource management to the cloud data warehouse. Many cloud data warehouses offer compute scaling that allows for dynamic scaling when needs spike. This allows data teams to still see scalable performance while holding increased numbers of computationally expensive data models and ingesting more large data sources. The decreased cost in compute power along with compute scaling in cloud data warehouses allows data teams to efficiently scale resources up or down to suit their needs and better ensure no downtime. The bottom line is that, instead of having your in-house data and/or IT team fretting over your data storage and computing issues, you can offload that pretty much completely to the data warehouse provider.

Data teams can then build tests on top of their cloud data warehouse to monitor their data sources for quality, freshness, etc. giving them quicker, more proactive visibility into any problems with their data pipelines.

ETL evolution

Highly evolved technologies have made a huge impact on the way data, analytics and BI solution designings since their inception. We have moved far ahead beydon excel tables, formulae, manual charting and on-prem centric architectures. Cloud-native data warehouses, cloud-native architecture in analytics and BI platforms, and embedded analytics powered by these systems have redefined what it means to be truly data-driven in our modern age.

The ETL process has been updated and can now deliver insights from a wide array of datasets, which helps companies and teams of all kinds make smarter decisions, faster. It also opens the doors for advanced analytics, next-level data monetization, and much more. Whatever you’re building with your data, a modern ELT setup will help you get more from your data, easier.

With constantly growing awareness of data analytics among businesses and ever improving data analytical technologies, in the future we might see high adoption of Data lakes as the initial destination for all raw data sources, offering even further cost benefits. Additionally, new tools are starting to take form that allow for transformation within these data lakes, thus continuing the evolution of tools and processes within the data pipeline following the path of ETL to ELT.

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