DATA ANALYTICS

How Big Data Infrastructure Powers BI

Julian Alvarado

Sr. Content Marketing Manager, Sigma

The term “big data” has become ubiquitous, and it’s only getting bigger — which is why more companies are investing in the infrastructure that supports big data. By 2023, the data industry will be worth an estimated $77 billion. Data is coming from everywhere, including mobile devices, apps, and IoT—and in structured, unstructured, and semi-structured formats.

While this data could be an invaluable source of business intelligence, and 81% of companies agree that data should be at the heart of all decision-making. most companies only analyze 12% of the data they have. Gaining insights from data requires not only having the data but also optimizing your big data infrastructure to best use it.

Let’s look at big data infrastructure’s three primary layers and the role they play in your business intelligence.

What Is a Cloud Data Analytics Stack?

The cloud data analytics stack is the set of tools, systems, networks, and environments that collect data, store it, transform it, and analyze it. This cloud data analytics stack can be broken down into three basic layers: the data pipeline, the data platform, and data exploration.

Layer 1: The Data Pipeline


Today’s data comes from four primary places:

  • Traditional databases like Oracle and DB2
  • On-prem enterprise apps
  • Databases like MySQL and MongoDB and streams like Kafka
  • Instrumentation, or user “events” collected and tracked in tools like Google Analytics
  • SaaS software, from CRMs to credit card processing platforms
  • IoT and device log files
  • Social and web logs and clickstreams
  • Public data, from Data-as-a-Service providers

Valuable data is being generated at an unprecedented pace in various formats. Before a company can use this data, it must be collected, integrated, and prepared for storage. A variety of data pipeline tools like Fivetran and Matillion automatically connect and normalize data from across sources in real-time, preparing it for storage and querying using analysis-ready schemas.

Layer 2: The Data Platform


Actionable analytics requires a single source of truth. Modern cloud data platforms (also known as cloud data warehouses) like Snowflake’s Data Cloud, BigQuery, and Redshift serve as a centralized repository for all data. Modern CDWs provide elastic infrastructure, unlimited scale, cost-effective risk mitigation, and security management. Which platform you choose will depend on your organization’s specific needs since each has different features and capabilities.

Getting the data into the CDW isn’t enough, however. The disparate data must be transformed so it’s queryable. Tools like dbt apply software engineering best practices, such as version control, to model your data for ingestion and broader use. Fivetran, for example, creates free dbt packages for common business challenges, which are a helpful starting point.

Layer 3: Data Exploration


Layer 3 is where the fun begins: exploration and analysis. To ensure that decision-makers can get the insights they need when they need them, companies must empower employees of all technical abilities to independently interact with data. Cloud-native BI solutions (like Sigma) allow every decision-maker to directly query live data, down to row-level detail, while maintaining strict data governance. With Sigma, teams can analyze billions of rows, create visualizations, join data sources, do rapid what-if analysis, and more via a familiar spreadsheet-like interface.

Where Companies Fall Short With Their Big Data Analytics Stack

Most organizations have a big data analytics stack. So why do 63% of decision-makers still report that they’re unable to get the answers they need in the required timeframe? The answer is that their stack isn’t optimized for their needs. Here are three reasons organizations fall short in enabling decision-makers to gain insights promptly and why your big data infrastructure must address these issues.

  1. Failure to Focus on the Cloud. Cloud data warehouses are a relatively recent innovation — they were born in response to the increasing plethora of data-generating platforms, applications, and IoT devices of the modern age. But most of today’s analytics tools were designed for on-premises warehouses and have been retrofitted as SaaS tools. These retrofitted tools just aren’t built to leverage the sheer scale and processing power of the cloud. Many require data aggregations or subsets of data for analysis, rather than working directly on millions or billions of data rows in your cloud data platform. Therefore loading, joining, modeling, and analyzing raw data at cloud scale is often easier said than done, often resulting in the software slowing down or crashing completely. For this reason, business teams have difficulty getting a complete, real-time picture of their data. And if they have follow-up questions, they must wait for the BI team to prep and model additional data. To prevent this issue, look for cloud-native tools that are built to accommodate the velocity of big data.
  2. Analytics Tools That Require Technical Skills. Many analytics solutions require SQL to drill into data, which means that decision-makers without SQL skills can’t conduct their own analysis. They must wait for the data team. As a result, decision-makers can’t get timely insights because they’re stuck waiting in the data team’s request queue. When this happens, desperate business teams access the data by extracting it to spreadsheets, which creates its own set of issues, including stale data, data silos, scale limitations, and governance and security risks. Instead, look for tools that allow non-technical users to explore the data in an intuitive interface that doesn’t require SQL or coding skills.
  3. Reliance on Static Dashboards. When non-technical users can’t conduct their own analyses, they’re usually limited to view-only metrics in surface-level, static dashboards. If they have additional questions or want to perform more in-depth analyses, they have to go to their data team and wait for answers. BI teams everywhere are overloaded, so this question-answer process can take days or even weeks to complete. This problem is also solved with tools that allow decision-makers to access live data in your cloud platform and explore the data without SQL or coding skills. Business users must be able to ask questions on-demand and drill down to record-level detail.

Modernize and Tailor Your Big Data Infrastructure

While the abundance of choice can be overwhelming, optimizing your big data infrastructure comes down to two key steps. First, modernize your big data stack by taking a cloud-native approach to ensure you’re able to take advantage of everything the cloud offers. Second, take a close look at your organization’s specific needs and tailor your stack accordingly. For example, consider how much data you’re generating, where it’s coming from, and who should be able to work with the data. By starting with your needs and objectives, you’ll have a clear picture of what you need in your stack.

Ready to visualize your data for actionable insights?