2023 Enterprise Data Stack: 9 Tools Your Company Should Have

2023 Enterprise Data Stack: 9 Tools Your Company Should Have

September 26, 2023

Table of contents

The necessity for lean, up-to-date, and operable enterprise data persists in digital commerce and the SaaS world. In fact, there’s no way to carry out any strategic planning and form a solid development strategy without business data analytics.

That said, unstructured and raw data must undergo complex multi-stage processing backed up with a modern data stack before data analysts lay their hands on it. An enterprise data stack is a suite of tools utilized to collect, store, transform, and extract data. The more precise, agile, and easy to deploy the company’s data architecture is, the less time and expenditure will be wasted in attempting to operationalize the existing data.

Given that, let’s consider what tools constitute an enterprise data stack, their functionality, and how exactly they ensure data accessibility and operability.

Enterprise Data in 2023: An Ever-Growing Demand for Modern Data Stack Solutions

For the past decade or so, enterprise data has grown tremendously in volume.  According to Statista, external repositories like cloud-based data warehouses have doubled the volume of stored enterprise data just between 2020-2022.

Migrating huge data arrays from rigid internal systems to fast and flexible cloud data warehouses and data lakes resolved many problems data engineers wrestled with before. It led to:

  • Increased processing speed. Legacy data systems lagged behind modern data stacks due to the lack of storage capacity and computing power. Cloud-based data warehouses increase processing speed manyfold as they utilize best-of-breed hardware solutions.
  • Simpler scalability. The modularity of modern data stack products provides rich capabilities to scale up storage capacity and add data integrations to an existing suite of tools.
  • Easier deployment. On average, it takes 20-30 minutes to set a cloud-based data stack ready and running. Modern data warehouses and data lakes will scan metadata details automatically and categorize them in a strict order.
  • Reduced cost. Switching to a modern data stack ecosystem is cost-effective as you no longer need to invest in hardware upgrades and data architecture modernization.

On top of that, building business processes upon a cloud data stack is commonly endorsed due to its vast automation capabilities. Machine learning and AI-powered tools easily integrate with modern data stacks and autonomously correct common data issues like duplicates and outdated records.

Data cleansing is crucial as it helps to keep the warehouse budget lean and, most importantly, brings clarity into data analytics and consequent decision-making. The latest study by IBM reveals that e-commerce businesses can lose up to 30% of their revenue if they rely on poor data.

An Enterprise Data Stack Overview

In substance, a modern data stack can be viewed as a warehouse-centered suite of tools. Cloud-based data storage is at the core of Modern Data Stacks and serves as a hub where data gets collected, streamlined, and represented in an edible form. 

This hub unit isn’t only a place where external data pipelines converge. It also feeds collected data to analytics and business intelligence (BI) tools and reverse ETL tools, allowing business teams to utilize the processed data in external applications like CRMs and martech tools.

5 Default Tools Modern Data Stack Builds Upon

Data Sources

On average, enterprises source data from at least 400 sources to get comprehensive insights into market changes and customer preferences. Modern data stacks are fully compatible with external sources (Salesforce, HubSpot, API integrations) and internal ones (CRMs, ERPs, website events, web or mobile application log files).

Extract, Transform, and Load (ETL) Tools

ETL tools source raw data and transform it into a standardized format, typically in tabular form, before depositing it into cloud storage. ETL tools ensure the clockwork functioning of modern data stack’s external pipelines but are also responsible for data cleansing.

During the transformation stage, raw data gets validated and checked for accuracy. Modern ETL processes are also AI-powered, which allows purging data from duplicates and irrelevant records and correcting incomplete records thanks to predictive algorithms.

Cloud Data Warehouse (CDW)

As modern data stack requirements evolve, more organizations opt for managed cloud data warehouses. It means that CDWs are managed by the vendor. They handle data architecture optimization by deploying additional computing and storage capacities.

The great thing about data warehousing is that business users pay for computing and storage separately. Conversely, you can’t scale up the repository separately from the computing capacity, and there’s only an option to upgrade them altogether. Therefore, managed CDW offers much more flexible and beneficial pricing.

Data Build Tools

Data build tools or DBT are open-source frameworks empowering data scientists to bring out insightful data infrastructure models that simplify analysis for data engineers. DBTs utilize SQL to build standardized models, which then can be used to optimize SQL code that extracts data from storage. In such a way, organizations can reach consistency and integrity of their actionable data and understand how it transforms over time.

Data Visualization & Analytics Tools

Visualization tools like Qlik or Tableau provide analytics and data experts with exploration views in the form of column or pie charts. With analytics and BI tools integrated into the data stack, users can build their own dashboards and make data exploration informative and insightful. 

Exploration views group and categorize data that specifies the number of sales and contracts, list of vendors, quarterly financing allocated to departments, etc. Looking into case-specific business activity metrics allows stakeholders to stay informed of organization performance and devise feasible plans for strategic improvements.

4 Types of Tools Worth Adding to Your Enterprise Data Stack

Apart from the core functionality of modern data stack, many compatible and configurable components provide additional control over data quality and reuse.

Here are examples of data stack components worth adding to a generic suite of tools.

CDPs and Event Pipes

Customer data platforms (CDPs) channel valuable behavioral data to CDWs each time customers interact with a SaaS website or mobile app. That’s why they are commonly referred to as “event pipes.”

Such behavioral signals help Marketing and Sales develop personalized value prepositions and effectively drive customer satisfaction.

Reverse ETLs

If your business demands sending collected data from CDW to an external application, you’ll need reverse ETL products. They form data pipelines that import data to business applications in a standardized, compatible format. These are typically marketing automation platforms, marketplaces, or ad networks.

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Data Lake Houses

Unlike data lakes that aggregate unstructured data (or structured and unstructured data side-by-side in native formats), data lake houses can read metadata. It allows them to add a data governance layer on top of data lake storage.

As a result, the lakehouse-based data stack supports BI and analytics tools that are compatible with CDWs. On top of that, data lakes don’t support concurrent transactions, which means the same files cannot be read and modified simultaneously. So, if you want to update essential records while keeping them readable, data lakehouses are a must-have for your modern data stack.

Data Observability & Data Operations Platforms

The practice shows that besides built-in data validation and cleansing functionality of ETL, the enterprise data stack requires proactive data quality monitoring. It immensely benefits data engineers and analysts as they can get instant insight into data health issues with detailed root cause reports.

Such proactive data quality monitoring solutions are helpful as they eliminate or correct flawed data before it reaches downstream users and affects critical business operations.

Why Data Operations Functionality Is a Must

Given the increasing complexity of data pipelines, co-dependency of digitalized workflows, and general data volume growth, enterprises need to handle data issues without delay. With decent data operations and data observability, companies can systematically monitor and improve the health of the stored data and investigate distortions and errors hidden behind the ETL process.

Eventually, such comprehensive data health observation pays off with:

  • The operative prevention of data issues. Automated monitoring detects, analyzes, and predicts the possible impact of incomplete or flawed data. It helps data engineers intervene before those actions cause actual disruption in data utilization.
  • Improved coordination among data specialists. The data issue reports pinpoint root causes to help data architects, data engineers, and DevOps determine which data lineage stage needs improvement. It brings more clarity into current infrastructure functioning and tips data specialists on optimizing it.
  • Data downtime prevention. Fixing data issues before they affect regular data use scenarios prevents unwanted downtimes and related revenue losses.
  • Increased data reliability. With healthy and 100% accurate business intelligence data, data analysts can accurately assess overall business performance and forecast market changes. Thus, stakeholders will get solid data to act upon in strategic planning.

How Revefi’s Data Operations Cloud Ensures High Quality and Reliability of Enterprise Data

Revefi’s cloud data monitoring allows you to govern a modern data stack with increased cost-efficiency. It eliminates data downtimes and ensures 5x times faster debugging through automated data quality monitoring with real-time alerts and AI-powered insights.

With Revefi’s Data Operation Cloud, you can benefit from:

  • Zero-touch deployment. Revefi integrates on top of CDWs and modern data stacks in minutes. Available data sets get to the exploration dashboard automatically.
  • Inspection of data issue root cause. AI-powered root cause analysis identifies the entire lifecycle of existing data quality issues.
  • Preventive data issue detection. Predictive algorithms will tip you beforehand on lingering issues that can impact data processing stability.
  • Full data security compliance. Refevi is a SOC2 Type 2 certified data monitoring product.

Obtain full-fledged control over data quality and optimize data storage ROI with efficient and simple-in-use data operations solutions. Try Revefi for free right now!

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