Data Infrastructure: Overview, Costs, & Top Integration Tools

What Is Data Infrastructure?

The data infrastructure is technological and organizational ecosystem to handle big data. Data infrastructure includes hardware, software, networks, databases and data centers. It plays a critical role in management and use of large volumes of data.

A well-designed data strategy can help organizations to efficiently store, access, and use their data and fulfill related business needs. This enables them to make informed decisions and gain insights into their operations.

Types of data infrastructure

There are several types of data infrastructures, such as:

NoSQL databases: NoSQL databases are designed to handle unstructured and semi-structured data. Examples: MongoDB, Cassandra, and HBase
Data warehouses: These are used to store large volumes of data from different sources in a central location. These are optimized for fast data retrieval and analysis. Examples: Amazon Redshift, Google BigQuery, and Snowflake
Data lakes: Data lakes store large amounts of unstructured and semi-structured data in its native format. They are used for data exploration and analysis, and are often used in big data analytics. Examples: AWS S3, Azure Data Lake Storage, and Google Cloud Storage
Data virtualization: It allows users to access data from multiple sources in real-time, without need of moving or copying the data. Examples: Denodo, Informatica, and Cisco Data Virtualization
Data integration platforms: Data integration platforms are used to combine data from multiple sources into a single unified view. They are used to improve data quality and reduce data redundancy. Examples: Talend, Informatica, Dell Boomi and Portable

Benefits of cloud data infrastructure

The cloud data infrastructure provides businesses with the ability to store and access their data and applications over the internet. This avoids need for on-premises infrastructure. Some benefits of cloud data infrastructure are:

Scalability: With cloud data infrastructure, businesses can scale up or down their computing resources as needed, without significant capital investment.
Cost effectiveness: Cloud data infrastructure can be more cost-effective than on-premises data infrastructure.
Reliability and availability: Cloud data infrastructure providers offer exceptional reliability and high availability (HA) with built-in redundancy and fail over mechanisms.
Security: Cloud data infrastructure typically have robust security measures in place. This includes firewalls, intrusion detection and prevention systems, encryption, and access controls.
Collaboration: Cloud data infrastructure enables teams to collaborate more easily and effectively, by providing access to the same data and applications from anywhere in the world.
Innovation: Cloud data infrastructure can help businesses innovate more quickly and effectively, by modern technologies and services, such as artificial intelligence, machine learning, and big data analytics.
Compliance: Cloud data infrastructure providers typically offer compliance certifications and regulatory compliance tools, which helps businesses meet industry regulations and standards.
Flexibility and accessibility: Cloud data infrastructure also provides businesses with the ability to store and share data across multiple regions and geographies, providing greater flexibility and accessibility.

Challenges in data management

There are several challenges in data management that organizations may face. They need proper data infrastructures to handle these challenges:

Data quality: Maintaining data quality can be challenging, as data can be incomplete, inaccurate, or inconsistent.
Data security: Data security is a critical issue in data management, as organizations need to protect their data from unauthorized data access, theft or loss.
Data integration: Data integration involves combining data from multiple and long-tail data sources to create a unified view of the data. This can be challenging due to differences in data formats, structures, and quality.
Data privacy: Data privacy regulations require that personal data is collected, processed, and stored in compliance with privacy laws. This can be challenging, as organizations need to establish policies and procedures for data privacy compliance.
Data governance: Data governance can be challenging to establish a comprehensive framework to addresses organization's data management needs. In most cases, an outside consultant can perform an audit and advise on next steps.
Data storage and retrieval: It becomes challenging to store and retrieve large volume of data efficiently.
Data analytics: Extracting insights from big data can be challenging. Processes for data analysis such as using data visualization tools, creating reports, and setting up predictive analytics are recommended.

Data Centers vs. Data Warehouses

A data center is a physical location for organization's IT infrastructure. The primary function of a data center is to provide secure, reliable, and efficient computing resources for operations. Data centers also go by a few other names such as mainframes, bare metal servers, and on-premises databases.

On the other hand, a data warehouse is a relatively new technology. It supports large-scale data storage system with data services. It consolidates data sets from multiple sources for analysis and reporting. Data warehouses are designed to use artificial intelligence to support decision-making. Today, data warehouses exist as a virtualized application hosted by cloud data providers — independent from a specific location or building.

Data centers and data warehouses have similarities, such as reliance on fast computing and robust storage. But, their primary functions are different. Data centers focus on providing computing resources for an organization's daily operations, while data warehouses are designed to support long-term analytical processes.

On-premises data centers

On-premises data centers are physical facilities owned and operated by organizations to house their computing resources and IT infrastructure. While they provide complete control over IT infrastructure, they can be expensive to set up and maintain.

On-prem data center costs include:

Real estate: leases, ownership costs
Connectivity: network backbones, remote access
Temperature control: HVAC, air filtration
Redundant power: batteries, diesel generators
Qualified talent: full-time and on-call network engineers

Given the on-premises upfront and on-going expenses, it's no wonder why cloud data warehouses are essential to modern data infrastructure.

Cloud data warehouses

A cloud data warehouse is hosted on a cloud computing platform. Following are some robust cloud data infrastructure providers:

Amazon Redshift: See pricing
Google BigQuery: See pricing
Snowflake: See pricing
PostgreSQL: See pricing

Top Data Infrastructure Tools

There are several data infrastructure tools available in the market. Here we provide an overview and cost of top data infrastructure tools.

We have categorized these tools based on three distinct processes 1) data integration, 2) data pipelines, and 3) data visualization.

Data Integration Tools

Data integration tool	Overview	Cost
Portable	Portable is the best data integration tool for teams dealing with long-tail data sources. Portable is an ETL platform that offers ETL pipelines and connectors for over 300 big data sources.	Free unlimited plan for manual data processing. $200/mo for scheduled data transfers.
Apache Kafka	A message broker project that aims to create a unified, high-throughput and low-latency platform with real-time data sources.	Apache Kafka is free and open-source. Support and maintenance is paid.
Apache NiFi	A web-based open-source data integration platform.	The Professional edition costs $0.25 per hour if purchased with an AWS account.
Pentaho	A powerful open-source platform for data integration and transformation.	30-day free trial. Pricing not available.
Stitch	A data pipeline tool integrated with Talend. It controls data extraction and simple manipulations using a built-in GUI.	14-day free trial, standard plan at $100/month, advanced package at $1,250/month, premium service at $2,500/month.
Airbyte	An open-source data integration tool that syncs data from apps, APIs and databases to data warehouses and lakes	Free plan available. Cloud plan: starting at $2.50/credit. Enterprise plan: price not available.
Microsoft SQL Server	Microsoft SQL Server Integration Services (SSIS) is a platform for developing high-performance data integration and workflow solutions.	SSIS comes in a variety of editions ranging from free to $14,256/core
Microsoft Azure Data Factory	Microsoft Azure Data Factory is a cloud-based data integration and data management tool.	Cost of read/write starts at $0.50 for every 50,000 modified/referenced entities. Monitoring begins at $0.25 per 50,000 run records obtained.

Data Pipeline Tools

Data pipeline tool	Overview	Cost
Apache Airflow	Apache Airflow is an open-source framework for authoring, scheduling, and monitoring processes programmatically.	Free, open-source
Blendo	Rudderstack acquired Blendo, a cloud data platform for no-code ELT and customer data pipelines.	Only three sources are free. The Pro package costs $750/month. Pricing for enterprise plans can be customized.
Stitch	Stitch, a data pipeline tool, is included with Talend.	14-day trial, standard plan starting at $100/month, advanced package at $1,250/month, premium service at $2,500/month.
AWS Glue	Amazon Web Services (AWS) Glue, a fully managed extract, transform, and load (ETL) solution, makes it simple to transport data between data storage.	Pay as you go: $0.44 per digital processing hour
Oracle Data Integrator	Oracle Data Integrator (ODI) is a data integration tool. It includes Oracle GoldenGate and Oracle Data Quality.	A single processor deployment costs around $36,400.
Kedro	Kedro is an open sourced Python framework for creating maintainable and modular data science code.	Free, open-source
Joblib	Joblib is a set of tools to provide lightweight data pipelines in Python.	Free, open-source

Data visualization tools

Data visualization tool	Overview	Cost
PowerBI	Microsoft Power BI is an interactive data visualization tool primarily focused on business intelligence.	Power BI Pro: $13.70/user monthly. Power BI Premium: $27.50/user monthly and from $6,858/capacity monthly.
Looker Studio	Google’s Looker Studio (formerly Data Studio) turns your data into informative, easily readable, and customizable dashboards and reports.	Free
Tableau	Tableau is a visual analytics platform with plenty of alternative options. From CSV files to Google Ads and Analytics data to Salesforce data, there are hundreds of data import choices for all stakeholders.	Tableau Reader and Tableau Public are free. Tableau Creator licenses are for $70/user monthly. Tableau Explorer licenses are for $42/user monthly. Tableau Viewer licenses are $12/user monthly.
Splunk	Splunk provides software for searching, monitoring, and analyzing machine-generated data via a web-style interface to enable strategic decisions.	Splunk Enterprise pricing is $150/month billed yearly.
Plotly	Plotly provides full interaction with analytics-focused languages such as Matlab, Python, and R, for elaborate visualizations.	The price is not available.
Domo	Domo is a cloud platform for conducting data analysis and creating interactive visualizations of vital metrics.	Free plan available. Three pricing plans ranging from $83 to $190.
Knowi	Knowi is an adaptive intelligence platform for modern data that unifies analytics across unstructured and structured data.	The price is not available.
Apache Superset	Apache Superset is a new platform for data exploration and visualization. It is an open-source alternative to popular enterprise analytics products.	For 10 users, fees range from $3,000 to $5,000.
Qlik	Qlik is a significant participant in the data visualization business. It serves over 40,000 clients in 100 countries.	Qlik Sense Business plans begin at $30 per user per month.

Modern Data Infrastructure Strategy

Today, an infrastructure strategy should be adopted to support data-driven decision-making and analytics within an organization.

A data infrastructure plan should not only be tailored to the specific use cases of the organization but following key elements should also be included:

Cloud-native architecture
Data warehousing and data lakes
Enterprise data needs
Business intelligence and analytics tools
Machine learning and AI tools
Data governance and security

Data science team charter

A data science team charter serves as a roadmap for the analytics team and helps ensure that everyone is aligned and taking initiatives to work towards the same objectives.

A data science team charter might include:

Purpose of the data science team
Goals the data science team has to target
Responsibilities the data science team has
Processes the data science team follows

Architecting the modern data stack

The modern data stack has following key architectural components.

No-code ETL / ELT / Reverse ETL:
- Simplifies data integration using visual interface
- Sync data from warehouses to business apps
- Built-in data cleansing and transformation to improve data quality
- Work with data without relying on IT or data engineers
- Make datasets more accessible to a wider range of expert/non-expert users
- Equipped with data flow automation
Data lakes:
- Store structured, semi-structured, and unstructured big data at any scale
- Designed to store raw data assets
- Manage and analyze data generated by IoT
- Can be reliable infrastructures with proper planning, implementation, and continuous management
Data visualization:
- Turn raw data into dashboards
- Enable quick and easy understanding of complex data sets
- Communicate complex information to a broad audience
Data governance:
- Measure and improve quality with right data
- Ensure data security
- Collaborate between stakeholders within an organization
- Developing policies and procedures for management through data lifecycle
- Provisioning of data resources

Optimize Data Management With Portable

Portable is the best data infrastructure tool for teams dealing with long-tail data sources.

It's a no-code ETL platform that offers connectors for 300+ uncommon data sources including many business SaaS apps with integrated data infrastructure. Try it free today!