Building A Modern Cloud Analytics Stack: A Playbook for Data-driven Companies

PLAYBOOK

Building A Modern Cloud Analytics Stack: A Guide for Data-driven Companies

The saying goes, “if you can’t measure it, you can’t improve it,” and to that end, most companies believe they can measure their way to success. But while many businesses understand the intrinsic value of the information contained within their data, generating holistic insights from it to drive the business is often easier said than done.

The average startup uses 20-50 paid SaaS applications to run their business and connecting the dots between them is difficult because of the time, effort, and technical expertise it requires. As a result, many startups can’t leverage the data from their SaaS applications easily — or don’t use it at all. According to one report, 56% of startups “rarely or infrequently” check their data, and 33% of those say the reason is that they have too many other responsibilities.

Fortunately, there are now technologies that provide the robust infrastructure required to make data available easily and quickly for organizations of any size. But with many options available to choose from, it can be challenging to determine the best solution for your needs.

Here, we discuss how to build a modern cloud analytics stack: the integrated set of tools and services that allows companies to unlock the full value of their data and enable the faster, smarter decisions necessary to compete and grow their business.

The Problem With Traditional Analytics Stacks

For years, business intelligence (BI) was a field that only enterprise companies could access due to the resource-intensive and expensive costs of building and maintaining the infrastructure required to power BI and analytics. But even large, analytically mature organizations still encounter barriers to organization-wide data-driven decision-making due to:

  1. Infrastructure. Most of today’s analytic systems and tools were designed for on-premise warehouses and retrofitted for the cloud. They often require data to be extracted for preparation and heavily modeled by the BI team before it can be used by the business and may still require some components to be run on-premises or to manually set up and managed.
  2. Access. Most analytic solutions are focused on reporting dashboards and require SQL or proprietary code to drill into data, which prevents non-technical business users from getting their hands on the data they need to make timely decisions. As a result, many organizations have to employ valuable engineering sources to help pull and combine datasets for analysis — time better spent developing products. And when engineers can’t get to the request in time, business users are forced to access the data the only way they know how: by extracting it to spreadsheets. This creates its own set of issues including stale data, data silos, scale limitations, and worst of all, governance and security risks.
  3. Dashboards. Domain experts are often limited to view-only metrics in surface-level, static dashboards, which prevent them from performing more in-depth analyses. If they have follow-up questions about the data, they must go back to their data or BI team — a cycle that can take days, if not weeks, to finally obtain useful insights. Growing businesses must remain agile to compete and cannot afford to wait that long.

But the rise of cloud data exploration — and the underlying tools and technology that support it — enables organizations of any size to take advantage of the cloud’s speed, accessibility, and near-infinite scale. All in an easy-to-implement, low-cost, hosting-free environment that saves time and resources, allowing growing businesses to more readily compete with the large enterprise corporations they are trying to disrupt.

The cloud analytics stack: A SaaS solution for fast-moving organizations of any size

The cloud data analytics stack refers to the layered ‘stack’ of technologies, cloud-based services, and data management systems that collect, store, and analyze data. It provides organizations with a steady stream of real-time data to generate value out of their data to power decision-making throughout the business.

The analytics stack is an effective end-to-end solution that manages where data comes from, how it moves around, and how it is prepared for analysis and consumption by end users. And when properly implemented, the modern cloud analytics stack delivers continuous data integration and organization-wide accessibility, with minimal manual intervention and bespoke code.

LAYER 1

The Cloud Data Pipeline

Part of what makes data so valuable is that it can provide a glimpse into business operations in real-time. But to benefit from that view, you need a pipeline that ingests and transforms data across applications, databases, files, and more into a centralized repository called the cloud data warehouse (also referred to as a cloud data platform), where it can then be housed modeled, and holistically analyzed.

How the cloud data pipeline works

Manually extracting and integrating data from your systems and applications to the warehouse is a headache and something no fast-growing company has time for. Large enterprise organizations have teams of dedicated data engineers that typically spend hours a week building and maintaining such pipelines within a business. They are tasked with handling data normalization, source changes, schema updates, and more.

Fortunately, there are now SaaS solutions that offer out-of-the-box connectivity to popular data sources, SaaS applications and more, as well as normalize and transform disparate sources of data and move it around without having to write code. When selecting a cloud data pipeline for your business, ensure that the solution, at a minimum, does these three things:

  1. Does the heavy-lifting with integrations and pre-built modeling tools
    Make sure your solution builds and continuously maintains its integrations to a vast array of sources, so you don’t have to. You should be able to connect all your data sources — including structured and unstructured data — to your pipeline in a few clicks. A good data pipeline should also deliver near zero-maintenance, ready-to-query schemas, flexible transformation, and basic modeling capabilities.
  2. Keeps data fresh in real-time
    Many pipeline tools copy records from the database, which causes version control issues when records get deleted. A good data pipeline should automatically check for and update your data sources to ensure that any changes made within a platform or to your data are available in real-time. This helps ensure that all necessary data is brought into the cloud data warehouse, insights are always fresh, and engineers can focus on more meaningful work instead of managing data.
  3. Offers a fully managed solution
    Fully managed integration solutions allow organizations to outsource and automate the entire process of building and maintaining a data pipeline. This helps ensure that your data is pumped reliably and cleanly into your cloud data warehouse while freeing up valuable engineering resources.

Here are some popular data pipelines

Fivetran

Fivetran fully automated connectors sync data from cloud applications, databases, event logs and more into your data warehouse. Their integrations are built for analysts who need data centralized but don’t want to spend time maintaining their own pipelines or ETL systems, allowing data teams to focus on what really matters: driving analytics for their business.

Visit www.fivetran.com  to learn more

Matillion

Matillion is data transformation purpose-built for the Snowflake Data Cloud and Amazon Redshift, and Google BigQuery cloud data warehouses, enabling businesses to achieve new levels of simplicity, speed, scale, and savings. Trusted by companies of all sizes to meet their data integration and transformation needs, Matillion products are highly rated across the AWS, GCP and Azure Marketplaces.

Visit www.matillion.com to learn more

LAYER 2

The Cloud Data Platform

Data may be the world’s most valuable asset, but siloed, stale, and constrained data will never provide the business value startups need to compete in today’s highly fragmented market.

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