You must have heard or come across the term Big Data quite often. Big Data can include all formats of data presented in huge datasets. It may seem challenging to manage large amounts of data with your existing data systems, which is why you must make the switch to Big Data tools.
In this article, understand a little bit about Big Data, followed by some of the best Big Data tools that will help you properly process and analyze vast datasets.
What are Big Data Tools?
Countless data sources across the globe are generating data at an unprecedented rate. The data can come to you in various formats that include structured, semi-structured, and unstructured. An overwhelming amount of data is referred to as Big Data.
Massive datasets of Big Data exceed the capacity of traditional tools for storage, processing, and analysis. Hence, specialized platforms have been developed to provide organizations with a comprehensive framework for utilizing this data. These platforms are also known as Big Data tools.
Top Five Big Data Tools
Now that you have understood what Big Data tools are, look at some of the best tools you can use to enhance your data operations.
Apache Spark is designed for distributed processing and connecting to multiple computers to streamline Big Data operations. Spark’s unified analytics engine has gained prominence for its efficiency and versatility in handling various data processing tasks. The engine supports real-time as well as batch processing of data, graph processing, and machine learning models. The Apache Spark engine can even address the limitations in data processing capabilities of Apache Hadoop’s MapReduce engine.
- Speed and Efficiency: This Big Data tool processes your data in memory, presenting you with quicker processing times than those found in disk-based systems. The analytics engine can run tasks up to 100 times faster than Hadoop’s MapReduce. Hence, Spark is a preferred platform for data analysis due to its ability to store and process large computations.
- Seamless Integration and Compatibility: Apache Spark offers compatibility with several programming languages like Java, Python, R, and Scala. It also integrates with other Big Data tools and technologies, including Hadoop Distributed File System, Apache Cassandra, and OpenStack Swift.
- Rich Toolset: With Apache Spark, you get several libraries and tools to manage Big Data. For structured data querying, you can use Spark SQL. The MLlib is helpful for processing machine learning tasks, while the GraphX API can be utilized for graphs and graph-parallel computation.
Pricing: Apache Spark is open-source and free for all users.
Deployment: Apache Spark runs independently on cluster modes. You can run clusters on Spark’s standalone cluster managers or YARN, Apache Mesos, or Kubernetes.
Google Cloud Platform (GCP) is one the most comprehensive cloud computing services that unifies various tools and services offered by Google. While not specifically designed as a Big Data tool, GCP incorporates several embedded Big Data tools in its ecosystem. One of the most prominent tools includes BigQuery, a fully-managed, petabyte-scale analytics data warehouse.
BigQuery is a powerful solution that can function as a Big Data platform. It offers you a robust serverless infrastructure for storing, querying, and analyzing massive datasets at impressive speed and efficiency. BigQuery’s unique architecture enables automatic scaling and quick data processing without worrying about infrastructure management.
- Easy Querying Interface: BigQuery provides a standard SQL interface for querying data, allowing you to create and delete various objects, customize user functions, and import data from diverse formats.
- Machine Learning Analytics: With BigQuery, you can incorporate built-in machine learning algorithms into your datasets and make advances in the field of predictive analytics. The platform also offers geospatial analysis capabilities, allowing you to conduct data modeling through latitude and longitude locations.
- An Array of Integrations: BigQuery can support multiple data formats, including CSV, Parquet, Avro, and JSON. You also get seamless integration with other Google Cloud services, such as Google Cloud Storage and Google Data Studios, to help process Big Data.
Pricing: BigQuery pricing has two components: storage and compute pricing. For the former, you incur charges for storing the data you loaded into BigQuery. The latter is further divided into on-demand pricing calculated per TiB of query data processed monthly and capacity pricing per slot-hour.
Deployment: To use BigQuery, you can create a project on the Google Cloud Console and enable the BigQuery API. Alternatively, you can also use the BigQuery Data Transfer Service through the Data Transfer API.
Apache Hadoop is one of the top open-source Big Data tools, offering distributed processing capabilities for large datasets across compute clusters. You can utilize this cost-effective and scalable tool for storing, processing, and analyzing vast amounts of data. Hadoop is capable of scaling from a single server to an extensive network of commodity computers. Its widespread adoption and support from major players across industries underline its continued relevance as one of the best Big Data tools.
Hadoop has two core components: the Hadoop Distributed File System (HDFS) and the MapReduce engine. The former facilitates distributed storage across multiple machines, providing your data with fault-tolerant and high-availability features. The latter employs the MapReduce programming model that enables parallel data processing across the cluster. Hadoop also comes with a comprehensive set of Big Data tools and technologies to cater to the analytical needs of various organizations.
- Cost-effective: Hadoop can be deployed on commodity hardware, eliminating the need for supercomputers. Since this tool distributes storage and workload, it results in low operational costs for you.
- Security: This Big Data tool implements HTTP servers and comes with POSIX-style file system compatibility and authorization for securing your datasets.
- Scalability: Hadoop can seamlessly handle massive amounts of structured and unstructured data. It also supports diverse types of commodity hardware and integrates with enterprise cloud providers.
Pricing: Hadoop is freely available for all users.
Deployment: This Big Data tool offers several packages that can be independently upgraded and deployed across various platforms.
Launched in 2008, Apache Cassandra is one of the Big Data tools best known for processing large volumes of different data formats. It has a highly dependable data storage engine, crucial for managing applications requiring extensive scalability and reliability. Widely acknowledged for its fault tolerance and support for massive expansion, Apache Cassandra has become one of the critical tools in the Big Data landscape today.
- Non-Relational Database (NoSQL): Cassandra is a NoSQL database offering features that are not found in other relational and NoSQL databases. These unique features can include continuous availability as a data source, data dispersion across different data centers, and several Cloud availability zones.
- Fault Tolerance: You get a built-in fault tolerance feature with Apache Cassandra for both cloud infrastructure and commodity hardware. It ensures your data’s integrity and availability even during hardware or network failures.
- Data Duplication: Cassandra’s data storage engine reduces delay in operations by facilitating duplication of your datasets across multiple databases. Thanks to its linear scalability feature, the platform allows you to add several nodes to duplicate data and manage large workloads. Since there is no single point of failure, this feature is particularly useful to preserve your data even during an entire data center outage.
Pricing: Apache Cassandra is an open-source NoSQL database management system that is free for all users.
Deployment: Apache Cassandra can be deployed on Amazon EC2 instances. You can also choose appropriate external hardware with expansive memory, CPU, network, and the required number of nodes for enterprise implementation.
Cloudera is one of the best Big Data platforms, offering a range of tools and services for efficient management and analysis of large datasets. It is a hybrid data platform that leverages components from Apache Hadoop for distributed data storage and processing. Cloudera adopts modern data architectures that cater to petabyte-scale datasets. You get unified data fabrics and pioneering data lakehouses powered by Apache Iceberg to manage and transform vast datasets.
- Real-time Monitoring: Cloudera provides a flexible platform for gathering data from different environments. With its DataFlow analytics platform, you can stream and monitor large volumes of data in real-time.
- Data Modeling Capabilities: This Big Data tool is capable of constructing and training machine learning models for advanced analytics. You also get advanced analytical tools to gain in-depth insights from your data.
- Unified Platform: Cloudera has integrations with HDFS, Apache Spark, Apache Hive, and several other data warehouse and database management systems. Thus, it enables you to have diverse data analytics and processing capabilities under one platform.
Pricing: It offers a pay-as-you-go model wherein you are required to only pay for the services you utilize. The prices vary for each of the services offered.
Deployment: Cloudera can be deployed on-premises. You can also run it on AWS, GCP, and Azure through the Cloudera Data Platform (CDP) Public Cloud services, getting flexibility in Big Data management.
How to Get the Most Out of Big Data Tools
In this digital age, data volumes are constantly on the rise. To manage huge datasets, you have to utilize Big Data platforms efficiently. The first step lies in collecting and consolidating data from all sources before loading the existing data into the platform. For this, you must assess all possible points where data is generated in your organization. Then, you should work towards unifying all the relevant data and ensuring its accuracy and usability. It may seem like a mammoth task, but platforms like Airbyte can make the process quick and easy, helping you get the most out of your Big Data platform.
Airbyte is a robust data integration and replication platform that allows you to load data from multiple sources and load it to a data warehouse or Big Data platform of your choice. You can set up a data pipeline by either choosing from their 350+ pre-built connectors or creating a custom one through their Connector Development Kit. The advantage of both ways is that you can establish a secure connection between your sources and destinations within minutes without writing a single line of code!
Once the data is loaded into your chosen data warehouse, you can utilize its capabilities to efficiently process various tasks and operations. This can include creating predictive models and studying the insights to make strategic decisions.
Big Data analytics play a crucial role in empowering your business to create optimized experiences for your stakeholders. With sophisticated Big Data tools, you can improve your strategies and align them with the latest trends in consumer behavior. You will also be able to boost operational efficiency by effectively dealing with bottlenecks.
Before selecting a Big Data tool, you must consider creating a strong data pipeline with Airbyte. It will not only allow you to gather all your data in one place but also ensure changes made at source can be timely migrated and reflected in your Big Data platform. Sign up for free and get started right away!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What is ETL?
ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.
What is ELT?
More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:
- Faster processing times and loading speed
- Better scalability at a lower cost
- Support of more data sources (including Cloud apps), and of unstructured data
- Ability to have no-code data pipelines
- More flexibility and autonomy for data analysts with lower maintenance
- Better data integrity and reliability, easier identification of data inconsistencies
- Support of many more automations, including automatic schema change migration
Here is our recommendation for the criteria to consider:
- Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
- Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
- Ability to build new connectors: all data integration solutions support a limited number of data sources.
- Support of change data capture: this is especially important for your databases.
- Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
- Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
- Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ?
- Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt
- Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
- Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
- Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.
Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.
What's unique about Airbyte?
Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required..
Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.
Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.
Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has about 300 data connectors and over 5,000 customers.
Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.
What's unique about Fivetran?
Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.
Here are more critical insights on the key differentiations between Airbyte and Fivetran
3. Stitch Data
Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.
Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.
What's unique about Stitch?
Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.
Other potential services
Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.
What's unique about Matillion?
Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.
Here are more insights on the differentiations between Airbyte and Matillion.
Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.
What's unique about Airflow?
Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.
Here are more insights on the differentiations between Airbyte and Airflow.
Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.
What’s unique with Talend?
What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.
Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.
What is unique about Pentaho?
What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.
However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.
Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.
Microsoft SQL Server Integration Services (SSIS)
MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.
Singer is also worth mentioning as the first open-source JSON-based ETL framework. It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.
Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.
HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.
Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: