Who are the top Big Data companies providing solutions and hiring in 2024? Discover the leading big data companies shaping the future!
Top big data companies can help businesses manage and make sense of one of the modern era’s most essential organizational commodities: their data. Big data companies employ analysts who are skilled in identifying patterns, trends, and correlations in enterprise data that may otherwise go unnoticed, helping businesses gain valuable insights into customer behavior, operational efficiency, and market dynamics.
We’ve highlighted the top 10 big data companies we believe will shape the way enterprise organizations work with data in 2024 and beyond, detailing their areas of focus and type of workplace they offer for new big data analysts or big data professionals looking to make a career change.
Headquarters: Redmond, WA
Industries of Focus: Small-to-medium business, enterprise, government, education
Employees: 238,000
Workplace Type: On-campus, hybrid, remote
Best For: New graduate, mid-career, executive
Microsoft offers a comprehensive suite of tools and services that empower organizations to effectively manage, process, and derive insights from large and complex datasets. Azure, Microsoft’s cloud computing platform, offers a range of services for big data analytics, storage, and processing, including Azure Data Lake Storage, Azure Databricks, and Azure HDInsight, as well as Microsoft Fabric.
The integration of popular tools like Hadoop, Spark, and various machine learning frameworks within the Azure ecosystem provides a scalable and flexible environment for big data workloads. Microsoft’s Power BI facilitates data visualization and business intelligence, enabling users to gain meaningful insights from their data. Microsoft’s SQL Server, Azure SQL Database, and Cosmos DB support large-scale data processing, making it a versatile choice for enterprises dealing with diverse data types.
Headquarters: Irvine, CA
Industries of Focus: Financial services, healthcare, manufacturing, retail
Employees: 2,900
Workplace Type: Onsite, hybrid, remote
Best For: New graduate, mid-career
Alteryx is a key player in the data analytics and preparation space—its user-friendly data platform and integrated environment enables data blending, cleansing, and advanced analytics, and makes big data management accessible to both data analysts and business professionals. The company is renowned for its ability to streamline complex data workflows, offering a visual and code-free interface for data preparation and blending. The platform’s automation capabilities also enhance efficiency, allowing organizations to process and analyze large datasets with ease.
Headquarters: Redwood City, CA
Industries of Focus: Healthcare, pharmaceutical, enterprise, government, education
Employees: 5,000+
Workplace Type: Remote-first, onsite
Best For: New graduate, mid-career, executive
Informatica has cemented its place in the big data landscape as a prominent data integration and management company. With a comprehensive suite of solutions, the company addresses the challenges associated with big data by enabling organizations to efficiently integrate, cleanse, and manage diverse datasets. The company’s Data Management Cloud platform offers data quality and governance tools that ensure the accuracy and reliability of big data.
Headquarters: Mountain View, CA
Industries of Focus: Government, education, healthcare, pharmaceutical, retail, technology
Employees: 156,500
Workplace Type: Onsite
Best For: New graduate, mid-career
Google is of course a pivotal player in the enterprise data space, with a longstanding dominance in big data, cloud services, data analytics, and infrastructure offerings. Google Cloud Platform (GCP) provides a robust and scalable environment for storing, processing, and analyzing massive datasets. Services like BigQuery enable organizations to run fast SQL queries on large-scale data, while Google Cloud Storage and Cloud Bigtable offer efficient storage solutions for diverse data types.
Headquarters: Bozeman, MT
Industries of Focus: Retail, manufacturing, financial services, education, government
Employees: 5,884
Workplace Type: Full remote
Best For: New graduate, mid-career
Snowflake is a cloud-based data platform that revolutionized data warehousing and analytics. What sets the offering apart is its architecture, designed for the cloud and built to effortlessly scale horizontally, accommodating large and diverse datasets. The platform allows organizations to store and manage structured and semi-structured data efficiently, facilitating seamless data sharing and collaboration across teams.
Snowflake’s unique multi-cluster, shared data architecture enables users to run complex queries without the need for extensive data movement or duplication. The platform’s elasticity and on-demand resource allocation contribute to cost-effectiveness, as users only pay for the resources they consume. The data platform’s compatibility with various data processing tools and languages, coupled with its robust security features, positions it as a vital solution for businesses seeking a scalable, flexible, and secure foundation for their big data analytics initiatives.
Headquarters: Santa Clara, CA
Industries of Focus: Retail, transportation, healthcare, pharmaceutical, education, government
Employees: 3,084
Workplace Type: Onsite, remote, hybrid
Best For: Mid-career, executive
Cloudera plays a crucial role in the big data landscape as a leading provider of enterprise data management and analytics solutions. Renowned for its distribution of Apache Hadoop and contributions to the Hadoop ecosystem, Cloudera offers a comprehensive platform that enables organizations to store, process, and analyze vast amounts of data efficiently.
The Cloudera platform extends beyond Hadoop to incorporate a broader range of big data technologies, including Apache Spark and Apache Impala, providing users with a versatile and integrated environment for advanced analytics. The platform’s emphasis on security and governance ensures that businesses can manage and protect their data effectively, a critical aspect in the era of increasing data regulations.
Headquarters: San Diego, CA
Industries of Focus: Retail, transportation, healthcare, pharmaceutical, education, government, manufacturing
Employees: 7,000
Workplace Type: Onsite, remote
Best For: Mid-career, executive
Teradata holds a significant position in the realm of big data as a leading provider of analytic data solutions. Known for its advanced data warehousing and analytics capabilities, Teradata empowers organizations to process and analyze large volumes of data for actionable insights. The company’s platform is designed to handle complex and diverse datasets, offering robust analytics tools, such as Teradata Vantage, that enable businesses to derive meaningful intelligence from their data.
The company’s parallel processing architecture and scalability contribute to its products’ high-performance analytics, making it a preferred choice for enterprises dealing with massive datasets. Moreover, Teradata’s focus on hybrid and multi-cloud deployments enhances flexibility for organizations seeking to leverage the benefits of big data analytics across various environments. With a legacy of providing powerful data solutions, Teradata remains a crucial player in the big data landscape, offering businesses the tools and infrastructure needed to harness the full potential of their data for strategic decision-making.
Headquarters: San Francisco, CA
Industries of Focus: Financial services, technology, healthcare, pharmaceutical, manufacturing
Employees: 4,000+
Workplace Type: Hybrid
Best For: Recent graduate, mid-career
Databricks is one of the more prominent big data companies to emerge as of late, commonly placed on par with Snowflake, despite offering a different type of big data offering. The company develops a unified analytics platform that combines the power of Apache Spark with collaborative and interactive tools. Known for simplifying the complexities of big data processing, Databricks provides a cloud-based environment that enables seamless data integration, exploration, and advanced analytics.
Its platform facilitates collaborative data science and machine learning workflows, promoting teamwork and efficiency. Databricks Delta, a key component, enhances data reliability and performance by providing ACID transactions and time travel capabilities.
The company’s commitment to open-source technologies and its contributions to the Apache Spark community further solidify its importance. By providing an integrated and scalable solution for big data analytics, Databricks empowers organizations to unlock insights from their data, fostering innovation and strategic decision-making in a rapidly evolving data landscape.
Headquarters: Armonk, NY
Industries of Focus: Government, education, retail, technology, healthcare, pharmaceutical, manufacturing, aerospace/transportation
Employees: 288,300
Workplace Type: Onsite, hybrid
Best For: Mid-career, executive
Big Blue is no doubt the veteran of the lot, playing a pivotal role in the big data landscape since its inception. These days, IBM offers a comprehensive suite of solutions and services that span the entire data lifecycle. With IBM Cloud Pak for Data, the company provides an integrated platform that facilitates data management, governance, and analytics.
IBM’s expertise extends to advanced analytics and artificial intelligence, demonstrated by Watson, its cognitive computing system, which enables organizations to extract valuable insights from large and complex datasets. The company’s commitment to open-source technologies is evident through contributions to projects like Apache Spark and Hadoop, showcasing its dedication to innovation in the big data ecosystem.
IBM’s long-standing presence in enterprise computing, coupled with its focus on hybrid and multi cloud environments, positions it as a key enabler for businesses seeking to harness the potential of big data for strategic decision-making, digital transformation, and the development of cutting-edge AI applications.
Headquarters: Austin, Texas
Industries of Focus: Government, education, enterprise, technology, manufacturing, retail, healthcare, pharmaceutical
Employees: 58,000
Workplace Type: Onsite, hybrid
Best For: Mid-career, executive
In today’s big data landscape, Hewlett Packard Enterprise (HPE) is considered the go-to enterprise provider of infrastructure solutions designed to support and optimize large-scale data processing and analytics. Through offerings such as HPE Ezmeral, the company addresses the challenges of managing and extracting insights from massive datasets.
HPE’s hardware, including servers and storage solutions, is designed to handle the diverse workloads associated with big data applications. Additionally, HPE’s focus on edge computing and its GreenLake cloud services contribute to the flexibility and scalability required for evolving big data requirements.
The company’s commitment to innovation, coupled with its emphasis on providing end-to-end solutions, positions HPE as an important player for organizations seeking robust infrastructure to support their big data initiatives, ensuring efficiency, scalability, and reliability in the rapidly evolving data ecosystem/value chain.
A big data analyst is a professional who specializes in examining and interpreting vast and complex sets of data to extract meaningful insights and support informed decision-making within an organization.
Big data analysts are essential for identifying patterns, trends, and correlations that may otherwise go unnoticed, helping businesses gain valuable insights into customer behavior, operational efficiency, and market dynamics.
In 1999, Big Data referred to one gigabyte (1 GB); these days, the term usually represents datasets petabytes (1024 terabytes), exabytes (1024 petabytes) or zettabytes (1024 exabytes) in size.
Big data is collected through various methods that capture and gather large volumes of diverse information—for example, sensors, devices, and online services/platforms that generate data in real-time (e.g., IoT devices, social media sites, and mobile applications). Additionally, structured data is often collected from traditional databases and transactional systems as part of the incoming data stream.
Big data is used across various industries and domains to derive meaningful insights, optimize processes, and inform decision-making. In business, organizations leverage big data to analyze customer behavior, enhance marketing strategies, and improve overall operational efficiency.
The 4 Vs of big data refer to Volume, Velocity, Variety, and Veracity. These four dimensions collectively characterize the complexity of big data, highlighting the need for advanced technologies and analytics approaches to derive meaningful insights from the vast and dynamic datasets that characterize the big data landscape.
Ideal database systems for big data are those designed to handle the specific characteristics of massive and diverse datasets. NoSQL databases, such as MongoDB, Cassandra, and Couchbase, are commonly used in big data applications due to their ability to manage unstructured and semi-structured data efficiently, while distributed databases like Apache Hadoop and Apache Spark are well-suited for big data processing and analytics, enabling parallel processing across computing clusters.
Microsoft is renowned for its comprehensive suite of cloud services and analytics tools; Alteryx is recognized for its user-friendly data blending and analytics platform; and Google is a powerhouse in cloud services and data analytics. Informatica is lauded for its data integration solutions, while Snowflake is acknowledged for revolutionizing data warehousing with its cloud-based platform.
Cloudera stands out with its enterprise data management solutions, and Teradata excels in advanced data warehousing and analytics. Databricks, a leader in unified analytics, and IBM, with its extensive suite of solutions, showcase their prowess in the big data arena. HPE is recognized for its infrastructure solutions supporting large-scale data processing, while newer players like Databricks and Snowflake focus on advanced technologies like cloud-based data warehouses, data lake houses, and delta lakes.
In short, all of the leading big data firms mentioned in this guide continue to make significant contributions to the big data landscape. Data professionals looking for a new opportunity or to advance their careers as a big data analyst will find that these top big data companies are leading the conversation in the enterprise sector.
Read the Top 15 Big Data Technologies to learn more about the software analysts at the top big data companies are using.
Leon Yen is a former staff writer for Datamation. He has been reporting on technology for over a decade and has written for CNET and BigThink. Before that, he was the co-founder and CEO of a cybersecurity startup, where he led the development of an industry-first cyber risk management platform. He has an MBA from the University of North Carolina, Charlotte, and a BS in Information Systems from the University of San Francisco.
Datamation is the leading industry resource for B2B data professionals and technology buyers. Datamation's focus is on providing insight into the latest trends and innovation in AI, data security, big data, and more, along with in-depth product recommendations and comparisons. More than 1.7M users gain insight and guidance from Datamation every year.
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