Big data comprises large volumes of information in the form of simple to complex data sets at tremendous velocity. Big data analytics tools are equipped to ingest information in all forms – structured to semi-structured to unstructured – and transform it for visualization and analysis so that organizations from small startups to large corporations can make sense of their data. In this article, we will look closely at big data and the best big data solutions on the market.

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This information is priceless for enterprises to improve product and employee performance and maximize ROI. With business intelligence and analytics being the primary driving forces of market strategy, big data analytics has proved to be a game-changer for companies that use it compared to those that have not jumped on the bandwagon yet.

This article takes a look at what defines big data, its benefits, and a list of the best big data solutions in the market. We will cover the below topics:

• Five V’s of Big Data
• Benefits
• The Best Big Data Solutions
  • Apache Hadoop
  • Apache Spark
  • Hortonworks Data Platform
  • Vertica Advanced Analytics Platform
  • Pivotal Big Data Suite
• Next Steps

Five V’s of Big Data

So, is all big data valuable? How can your organization derive value from it? Data scientists determine the value of big data against certain attributes, commonly referred to as the five V’s.

• Volume: The volume of data generated determines if it is indeed big data, since “big” is a relative term. This metric can help organizations decide if they need a big data solution for processing their proprietary information.
• Velocity: How quickly data is generated and how fast it moves across systems will determine its usefulness.
• Variety: Nowadays, data is pulled from a variety of sources that include websites, applications, social media sites, audio and video sources, smart devices, sensor-based equipment and more. These disparate nuggets of information are part of enterprise business intelligence.
• Veracity: Data gleaned from disparate sources can be incomplete, inaccurate and inconsistent. Only data that is complete, accurate and consistent can add value to enterprise business intelligence and analytics.
• Value: The value that big data imparts to business decisions decides how useful it is for the organization.

Because of the sheer volume and variety of big data, it needs to be handled in a different way than traditional methods. Large, complex data sets need to be cleaned, prepared and transformed before they can be ingested by business intelligence and analytics solutions. Big data needs smart storage solutions and extremely fast computing speeds in order to produce real-time data insights.

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Primary Benefits

Get the Complete Picture

The variety of big data sources can be mind-boggling, with companies pulling data from on-premises and in-cloud data warehouses, data lakes, audio, video and text files, social media sites, IoT devices and more. With big data solutions, organizations can get the complete picture of their businesses, including day-to-day operational metrics and historical reports. With out-of-the-box functionality to clean, blend, prepare and transform data into ingestible information, big data solutions keep information enterprise-ready for reporting and analysis. With in-memory processing, data replication, low-latency writes and query optimizers, these solutions enable quick insights for proactive decision-making.

Innovate

The promise of big data solutions to deliver vital business insights prompts many enterprises to adopt them to track key metrics and get ahead of the competition by enhancing their business offerings. In addition to introducing improvements in their existing services and products, companies can explore the feasibility of introducing new products through market analysis by customer segment, region or country. What’s more, these solutions enable brand management through customer behavior and sentiment analysis that helps drive product strategy, providing excellent user experiences.

Increase Revenue

By 2027, big data market revenue is expected to increase to 103 billion U.S. dollars. Distributed data storage, multi-cloud clusters and massively parallel processing ensure that the latest data is made available to you when needed. With big data insights available in real time, businesses can make timely decisions to maximize revenue and ensure faster time to market. They can improve productivity through workforce data analysis and monitor product performance on a day-to-day basis, or over a specific period through time-series analyses. By simulating what-if scenarios, decision-makers can view trend forecasts and make decisions that help boost revenue.

Boost Employee Productivity

Big data solutions help glean real-time key performance metrics that enable organizations to set goals for employees. These metrics can be made visible, perhaps through large display screens in the office, or shared in team meetings to encourage employees to keep their eyes on the ball, so to speak. Workforce management software can highlight interesting insights like the most productive employees, as well as unproductive apps and websites. Workforce metrics can also surface critical health concerns, such as stress or depression, in an underperforming employee, which can help team managers take timely remedial action.

Detect Fraud

With the amount of data that businesses need to move across systems daily, information security can be a persistent concern. A major advantage of analyzing such humongous amounts of data is that it becomes easier to spot trends and patterns. This can be especially useful when sensitive information is at risk, such as personally identifiable information. Big data solutions help spot outliers and anomalies in data, such as hacking attacks, or, say, a suspicious spending pattern on a credit card that alerts the bank authorities even before the user becomes aware that something is amiss.

Best Big Data Solutions

The following is our list of top big data solutions based on a thorough analysis of features and benefits:

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Apache Hadoop

Overview

Apache Hadoop is a free-to-use, open-source distributed file system built to enable the lightning-fast processing of big data stored across clusters and scales seamlessly as per enterprise requirements. Deployable in the cloud and on-premises, it supports NoSQL distributed databases (such as HBase), which allow data to be spread across thousands of servers with little or no impact on performance. Its components include the Hadoop File Distribution System (HDFS) that enables storage, MapReduce that handles data processing and Hadoop YARN (an acronym for Yet Another Resource Negotiator) to manage computing resources in clusters.

Hadoop YARN Architecture.

Top Benefits

• Reliable Data Access: With support for file sizes from gigabytes to petabytes stored across multiple systems, data replication ensures reliable access to proprietary information. Cluster-wide load balancing is accompanied by even data distribution across disks to support low-latency data retrieval.
• Local Data Processing: Hadoop distributes files across separate cluster nodes and transfers the packaged code into them, enabling local data processing in parallel.
• Scalability: It provides high scalability and availability to businesses, with failure detection and handling at the individual application level itself. New YARN nodes can join the resource manager to execute jobs, and nodes can be decommissioned equally seamlessly to downscale the cluster.
• Centralized Cache Management: Users can have the software cache desired data blocks on separate nodes by specifying the paths from a centralized system. Through explicit pinning, users can retain a limited number of block read replicas in the buffer cache, while the rest are removed for optimal memory utilization.
• File System Snapshots: Point-in-time snapshots of the file system record the block list and the file size while Hadoop ensures data integrity by not copying the actual data. It records modifications to the file system in reverse chronological order so that current data is easily accessible.

Primary Features

• Programming Framework: Developers can build data processing applications that enable compute jobs on multiple cluster nodes. Users can perform a rolling upgrade of the MapReduce framework by running a different version from the one initially deployed through distributed cache deployment.
• Native Components: The Hadoop Library contains components that are deployed natively for enhanced performance, such as compression codecs, native IO utilities for purposes such as centralized cache management, and checksum implementations.
• HDFS NFS Gateway: HDFS can be mounted as part of the client’s local file system to enable local browsing of HDFS files, with the option to download and upload files to and from the local file system.
• Memory Storage Support: HDFS supports writing to off-heap memory, thus flushing in-memory data to the disk and keeping the performance-sensitive IO path clear. Referred to as Lazy Persist Writes, these data offloads help reduce query response latency.
• Extended Attributes: User applications can link additional metadata with a file or directory through extended attributes to store additional information about inodes.

Limitations

• It doesn’t support real-time data processing, only batch processing. As a result, its overall performance is slower.
• It doesn’t support cyclic data flow, so it isn’t very efficient with iterative processing.
• It doesn’t enforce data encryption at the storage and network levels. It adopts Kerberos authentication for security, which is difficult to maintain.

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Apache Spark

Overview

Apache Spark is an open-source computing engine that can perform batch as well as real-time data processing and as such, has a definite edge over Hadoop. Spark’s “in-memory” computing is key to its lightning-fast computing speed, with intermediate data stored in memory and reduced number of read/write operations to disk. Built to enhance Hadoop’s stack, it is supported by Java, Python, R, SQL and Scala. Extending the MapReduce model, Spark handles interactive queries and stream processing seamlessly faster than the speed of thought.

Graph Analytics Pipeline in Spark.

Top Benefits

• Deployment: In addition to running on Apache Mesos, YARN and Kubernetes clusters, Spark deploys as a standalone that can be launched manually or through launch scripts. Users can run its various daemons on a single machine for testing.
• Spark SQL: With access to Hive, Parquet, JSON, JDBC and a variety of data sources, Spark SQL enables data querying through SQL or a DataFrame API. With support for HiveQL syntax, Hive SerDes and UDFs, it enables access to existing Hive warehouses and connectivity to business intelligence tools.
• Streaming Analytics: With the ability to read data from HDFS, Flume, Kafka, Twitter, ZeroMQ and custom data sources, it supports efficient batch and stream processing, joining streams against historical data or running ad-hoc queries on data coming in in real time.
• R Connectivity: SparkR is a package that allows users to connect R programs to a Spark cluster from RStudio, RSHELL, Rscript or other R IDEs. In addition to a distributed data frame that supports selection, filtering and aggregation on large datasets, it enables machine learning using MLlib.

Primary Features

• Architecture: In addition to RDDs, the Spark ecosystem consists of Spark SQL, Scala, MLlib and the core Spark component. It has a master-slave architecture with a driver program that can run on a master node or a client node and a number of executors that run on worker nodes in the cluster.
• Spark Core: Its core processing engine enables memory management, fault recovery, scheduling, distribution and monitoring jobs on a cluster.
• Abstraction: Spark allows smart reuse of data and variables through resilient distributed datasets (RDDs), a collection of elements partitioned across nodes for parallel processing. Users can also ask for an RDD to persist in memory for later reuse. Spark provides another abstraction – the sharing and reuse of variables that are used to cache values in memory, or that act as counters and sums in computations.
• Machine Learning: Spark provides ML workflows that include feature transformation, model evaluation, ML pipeline building and more, through algorithms for clustering, classification, modeling and recommendations.

Limitations

• Security is OFF by default, so its deployments can be vulnerable to attack if not configured properly.
• Its versions don’t seem to be backward compatible.
• It occupies a large amount of memory space due to its in-memory processing engine.
• Its caching algorithm is not built-in and has to be set up manually.

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Hortonworks Data Platform

Overview

Yahoo set up Hortonworks in 2011 to facilitate Hadoop adoption by enterprises. Its Hadoop distribution, the Hortonworks Data Platform (HDP), is fully open-source and free, and offers competitive in-house expertise, an attractive incentive for enterprises looking to deploy Hadoop. It comprises of Hadoop projects including HDFS, MapReduce, Pig, Hive and Zookeeper. HDP is known for its purist approach to open-source and comes with zero proprietary software – it is open-source all the way, with Ambari for management, Stinger for query handling and Apache Solr for data searches. HCatalog is an HDP component that enables connecting Hadoop to other enterprise applications.

Hortonworks Architecture.

Top Benefits

• Deploy Anywhere: It deploys on-premises, in the cloud (as part of Microsoft Azure HDInsight) and as a hybrid solution, Cloudbreak. Designed for enterprises with existing on-premise data centers and IT infrastructure, Cloudbreak provides elastic scaling for resource optimization.
• Scalability and High Availability: Businesses can scale up to thousands of nodes and billions of files with NameNode federation. NameNodes manage the file path and mapping metadata, and federation ensures that they are independent of each other, ensuring higher availability at a lower total cost of ownership. In addition, erasure coding boosts storage efficiency greatly, allowing for more efficient data replication.
• Security and Governance: Apache Ranger and Apache Atlas enable data lineage tracking from its origin to the data lake, allowing for robust audit trails for proprietary information governance.
• Faster Time to Insight: It empowers businesses to roll out applications within minutes, reducing the time to market. It allows incorporating machine learning and deep learning into applications through graphics processing units (GPUs). Its hybrid data architecture offers cloud storage for limitless data that is in its original format, including ADLS, WASB, S3 and GCP.

Primary Features

• Centralized Architecture: With Apache YARN at the backend, Hadoop operators can scale their big data assets as necessary. YARN seamlessly allocates resources and services to applications distributed across clusters for operations, security and governance. It enables businesses to analyze data pulled from a multitude of sources and in a variety of formats.
• Containerization: With built-in YARN support for Docker containers, third-party applications deploy faster to run on Apache Hadoop. Users can run multiple versions of the same application to test, without impacting the existing one. Add to this the inherent benefits of containers – resource optimization and greater task throughput – and you have a competitive product.
• Data Access: YARN makes it possible for disparate data access methods to co-exist in the same cluster against shared data sets. HDP leverages this capability to allow users to interact with a variety of data sets simultaneously in a variety of ways. So business users can perform data management and processing within the same cluster through interactive SQL, real-time streaming and batch processing, thus eliminating data silos.
• Interoperability: Built from the ground up to offer a purely open-source Hadoop solution to enterprises, HDP integrates easily with a wide range of data centers and BI applications. Businesses can connect their existing IT infrastructures to HDP with minimal effort, saving expense, effort and time.

Limitations

• It doesn’t come with proprietary tools for data management. Organizations need additional solutions for query handling, searches and deployment management.
• It is quite difficult to use the Kerberized cluster and implement SSL.
• It isn’t possible to add security restrictions to data on Hive, an HDP component.

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Vertica Advanced Analytics Platform

Overview

Part of Hewlett Packard Enterprises (HPE) since 2011, Vertica was brought under Microfocus after the MicroFocus-HPE merger in 2017. While both Hadoop and Vertica Analytics Platform are scalable, big data solutions with massively parallel processing, Vertica is a next-generation relational database platform with standard SQL and ACID transactions. These two quite often complement each other in business solutions, with Hadoop providing batch-processing and Vertica Analytics Platform enabling real-time analytics. They work in tandem through multiple connectors that include a bi-directional connector for MapReduce and an HDFS connector to load data into the Vertica Advanced Analytics platform.

Vertica in Enterprise and Eon Mode.

Top Benefits

• Resource Management: Users can enable concurrent workload runs at an efficient pace through its Resource Manager. In addition to reducing CPU and memory usage, and disk I/O processing time, it compresses data size by up to 90%, without loss of information. Its massively parallel processing (MPP) SQL engine provides active redundancy, automatic replication, failover and recovery.
• Flexible Deployment: A highly performing analytical database, it can be deployed on-premises, in the cloud and as a hybrid solution. Flexible and scalable, it is built to run on Amazon, Azure, Google and VMWare clouds.
• Data Management: Its columnar data storage makes it the ideal choice for read-intensive workloads. Vertica supports a variety of input file types with an upload speed of many megabytes/second per machine per load stream. With multiple users accessing the same data concurrently, it controls data quality through data locking.
• Integrations: It helps analyze data from Apache Hadoop, Hive, Kafka and other data lake systems through built-in connectors and standard client libraries that include JDBC and ODBC. It integrates with BI tools including Cognos, Microstrategy, Tableau, ETL tools like Informatica, Talend and Pentaho.
• Advanced Analytics: Vertica combines the basic functions of a database with analytics capabilities like machine learning and algorithms for regression, classification and clustering. Enterprises can leverage its out-of-the-box geospatial and time-series analysis capabilities for immediate turnarounds on incoming data without needing to acquire additional analytics solutions.

Primary Features

• Data Preparation: Users can load and analyze not only structured data but also semi-structured data sets through flex tables.
• On Hadoop: Vertica for SQL installs directly on Apache Hadoop to provide powerful querying capabilities and analytics. It reads native Hadoop file formats like Parquet and ORC files and writes back to Parquet.
• Flattened Tables: It enables analysts to write fast-running queries and perform complex JOINS through flattened tables. These are decoupled from the source tables, so any changes done in one table do not impact the other. This enables quicker big data analysis in databases with complex schemas.
• Database Designer: Its database designer enables performance-optimized design for ad-hoc queries and operational reporting through SQL scripts that can be deployed automatically or manually.
• Workload Analyzer: It optimizes database objects through system table analysis with tuning recommendations and hints. It enables root cause analysis through workload and query execution history, and resources and system configurations.

Limitations

• It doesn’t support enforcing foreign keys and referential integrity.
• It doesn’t support automatic constraints on external tables.
• Deletion is time-consuming and can delay other processes in the pipeline.

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Pivotal Big Data Suite

Overview

Pivotal Big Data Suite is an integrated data warehousing and analytics solution owned by VMWare. Pivotal HD, its Hadoop distribution, includes YARN, GemFire, SQLFire and GemFire XD, an in-memory database with real-time analytics capabilities on top of HDFS. With a full-featured REST API, it supports SQL, MapReduce parallel processing and data sets up to hundreds of terabytes.

Pivotal Greenplum is cloud-agnostic and deploys seamlessly on public and private clouds that include AWS, Azure, Google Cloud Platform, VMWare, vSphere and OpenStack. In addition to automated, repeatable deployments with Kubernetes, it offers stateful data persistence to Cloud Foundry applications.

Pivotal Greenplum Architecture.

Top Benefits

• Open-Source: Aligned with the open-source PostGreSQL community, all contributions to the Greenplum database project share the same MPP architecture, analytical interfaces and security attributes.
• High Availability: Pivotal GemFire comes with automatic failover to other nodes in the cluster in case of job fails. Grids automatically rebalance and reorganize if nodes leave or join the cluster. WAN replication enables multi-site disaster recovery deployments.
• Advanced Analytics: Pivotal Greenplum offers machine learning, deep learning and graph, text and statistical methods in a scalable database, with ubiquitous support for R, Python, Keras and Tensorflow. It provides geospatial analytics based on PostGIS, and GPText provides text analytics with Apache Solr.
• Faster Data Processing: GemFire’s horizontal architecture, coupled with in-memory data processing, is designed for low latency application requirements. Additionally, queries are routed to nodes that have the relevant data for reduced response time, and query results display in a data table-like format for easier reference.

Primary Features

• Architecture: Its “shared-nothing” architecture with independent nodes, data replication and persistent write-optimized disk stores help keep processing time to a minimum.
• Integrations: Greenplum integrates with Kafka to provide faster event processing for streaming data through low-latency writes. It enables SQL-powered ad hoc queries and predictive analytics on data stored in HDFS, and machine learning through Apache MADlib. It enables better data integration in the cloud through Amazon S3 object-querying in place.
• Scalability: Pivotal GemFire helps maximize efficiency and keep steady-state runtime costs low by allowing users to scale out horizontally and back down again as necessary.
• Query Optimizer: With a fast-paced query optimizer, it enables faster computing in parallel on petabyte-sized data sets through selection of the best possible query execution model.

Limitations

• The latest version of Greenplum doesn’t bundle cURL and instead loads the system-provided library.
• MADlib, GPText and PostGIS aren’t available for Ubuntu systems.
• Greenplum for Kubernetes isn’t provided for the latest release.

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Next Steps

All the solutions discussed here are centered around Hadoop, a leading solution in the big data market. Apache Spark enhances Hadoop’s performance with its lightning-fast computing, whereas Vertica Advanced Analytics complements Hadoop’s batch processing with stream computing and analytics. Hortonworks Data Platform and Pivotal HD are Hadoop distributions that extend its capabilities as a data platform.

What are your big data requirements? If you are not sure where to start, check out our requirements template to guide you through the decision process. Or, take a look at the leading big data solutions with our comparison report for wider market research.

Have you used any big data solutions, or are considering one? Did we miss out on your preferred one? Let us know in the comments!