In the age of exponentially growing digital information, cloud data services tools have become a one-stop solution to help businesses store, process, and manage data. The associated benefits of scalability, efficiency, and accessibility have made these tools a popular choice for varied data needs. Cloud data services tools are available for almost every task involved in managing data, from data storage, integration, and visualization to analytics and business intelligence.
In this article, we will discuss the ten best Cloud Data Service tools that are available in 2024, ranging from data engineering to data science.
10 Cloud Data Services Tools
Here are ten cloud data services tools for organizations to perform data management efficiently:
1. Best Cloud Data Services: Hevo (ETL)
Hevo is a popular data integration tool that allows you to replicate data in real time. Using this platform’s extensive library of pre-built connectors, you can connect various data sources to a destination, such as a data warehouse. Hevo leverages many components of the AWS cloud for its infrastructure. Therefore, you can process billions of records and automatically scale up and down based on your workload requirements.
Additionally, Hevo’s intuitive user interface makes it ideal for non-technical and technical users to perform most of the data management services in the cloud with a few clicks.
Key features of Hevo:
- Pre-built Connectors: Hevo has a massive library of built-in connectors made by their engineering staff to streamline the connection of data sources to destinations. With integration with 150+ Data Sources (40+ free sources) and 11 destination connectors, you can collect data from almost anywhere and manage it in the destination of your choice.
- Data Transformation: With Hevo, you can control how you load data to the destination. It offers two types of data transformations: Python-based and drag-and-drop. Using Python-based transformation, you can use custom code to meet your unique data requirements. On the other hand, Hevo’s user-friendly drag-and-drop transformation allows you to clean, format, and filter data with just a few clicks.
- Automated Schema Management: Hevo automates the process of schema management. It automatically changes the schema in the destination as your data source evolves or changes. This ensures your data consistency regardless of how data evolves over time.
- Change Data Capture: CDC is the process of identifying and capturing the changes made in a system where data is stored. Since Hevo supports CDC, it allows you to automatically track changes in data over time and avoid manual work.
Hevo offers three distinct pricing plans: Starter, Business, and Free. The starter plan allows you to move all data from databases and business tools with a subscription of $239/month, and the Business plan is customizable.
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2. Best Cloud Data Services: AWS Glue (ETL)
AWS Glue is a serverless data integration tool provided by Amazon Web Services. Its serverless architecture allows you to ignore the intricacies of maintaining infrastructure and focus on more important tasks. With AWS Glue, you can connect with more than 70 data sources and manage your data in its native centralized catalog. Its user interface allows you to visually run, create, and monitor ETL pipelines in your chosen data lakes or warehouses.
AWS Glue includes many complex data management services in one cloud platform. This includes data discovery, cleansing, modern ETL, transforming, and centralized cataloging.
Key Features of AWS Glue:
- Data Catalog: AWS has a data catalog that allows it to store metadata of data you are working with. It includes definitions of processes, data tables, schema changes, and information about the ETL environment. This allows the creation of efficient data transformations and lineage tracking.
- Data Cleaning and Deduplication: AWS Glue uses machine learning models to find data duplicates. All you have to do is provide a small sample of data, and the model will be trained to perform the deduplication in the ETL process.
- Scheduling: With AWS Glue, you can regularly schedule ETL jobs in response to a certain event or on demand. In case of errors, it will simply restart the job and log it in its supporting tool.
AWS Glue offers different pricing plans according to the services you use. ETL jobs and development endpoints charge $0.44 per hour, data catalog storage and requests cost $1/Month. Additionally, crawlers and DataBrew interactive sessions cost $0.44 per session, and DataBrew jobs cost $0.48 per hour.
3. Best Cloud Data Services: Amazon S3 (Data Lake)
Amazon Simple Storage Service, or S3, is a widely known cloud data storage service. It is an object storage service that offers industry-leading data availability, scalability, performance, and security. Data-driven organizations mostly use it as a data lake. However, you can use S3 to store and protect data for many use cases, including backup and restore, enterprise applications, archives, big data analytics, and IoT devices.
If you are searching for the perfect combination of ETL and cloud data lake services, Amazon Glue and S3 go hand-in-hand.
Key Features of S3:
- Storage Management: With S3, you have total control over your data storage and management. You can specify how S3 buckets keep your information, what storage class it keeps your data in, whether it encrypts or compresses it, and more.
- Access Management: S3 features, like Access Control Lists (ACL) or S3 bucket policies, help you control who can access your data. ACL allows you to grant granular access to groups and individuals. On the other hand, bucket policies help to enforce fine-grained access control in bucket objects.
- Storage Logging and Monitoring: Amazon S3 gives you every detail of requests made to your bucket. It gives you information about when the request was made, the request method, path, and its response. You can use this feature to track data access patterns, troubleshoot errors, and analyze data trends.
There are a lot of factors involved in S3 pricing, but mainly, it depends on the storage you require and the features you use.
4. Best Cloud Data Services: Snowflake (Data Warehouse)
Snowflake is a software-as-a-service platform that provides most data management services in one platform. These services include data warehousing, data lake, and engineering. However, It is widely used as a data warehouse, leveraging the infrastructure of cloud platforms such as Microsoft Azure, Google Cloud Platform, and AWS. What sets Snowflake apart from other tools is its automatic scalability for computing resources. Therefore, you can run multiple workloads in parallel without worrying about resource contention.
Key features of Snowflake:
- Cloning: Snowflake allows you to create an instant copy of a Snowflake object, including a database, tables, and schema. Snowflake architecture makes it possible by storing data immutable in S3 and versioning changes as Metadata.
- Time Travel: Snowflake allows you to track historical changes in data over time. Using this time travel feature, you can find out how the database table looked at any point. This is valuable for efficiently maintaining data, auditing, and recovering from accidental changes. Additionally, you can use this as a cloud data recovery service in times of data loss.
- Semi-structured Data: Snowflake supports semi-structured data in various formats without using complex technologies like Hadoop or Hive. The formats include JSON, XML, Avro, ORC, and Parquet. This feature makes it ideal for modern data analytics and processing tasks, especially when the data is from different sources.
Snowflake offers four pricing plans: Standard, Enterprise, Business Critical, and Virtual Private Snowflake (VPS). Standard costs $2 per credit, Enterprise $3 per credit, Business critical $4 per credit, and VPS is customizable.
5. Best Cloud Data Services: Google BigQuery (Data Warehouse)
Google BigQuery is a cloud-based data warehouse that is built using BigTable and Google Cloud Platform. Using a serverless architecture, it helps you manage and analyze data efficiently. The distributed analysis engine of BigQuery lets you query terabytes in seconds and petabytes in minutes. It also supports SQL queries. Therefore, if you are familiar with SQL, you shouldn’t have much trouble using BigQuery to its full extent. Unlike most cloud data storage services, it provides multiple ways to access its environment, including web UI, a command line tool, or a client library.
Key features of Google BigQuery include:
- Serverless Infrastructure: The serverless architecture of BigQuery eliminates the need for manual work and automates infrastructure management. This lets you focus more on querying and analyzing data than the underlying infrastructure.
- SQL Compatibility: The writing and execution of queries in BigQuery are carried out in SQL syntax, which makes it easy for most developers to work with it.
- Federated Queries: BigQuery uses federated queries that let you send query statements to the storage system and get results as a temporary table. You can use this feature with Google Sheets, Google Cloud Storage, Google Cloud Dataflow, APIs, databases, and more.
BigQuery pricing is divided into two sections: Computing Pricing and Storage Pricing. Both have their costs according to your use case.
6. Best Cloud Data Services: Azure Synapse Analytics (Analytics)
Azure Synapse is an enterprise analytics solution provided by Microsoft. As the next iteration of Azure SQL data warehouse, it is mainly designed to serve as a data warehouse. However, it is essential to note Azure Synapse is not just for data warehousing. It includes data warehousing along with other solutions.
This tool provides a unified analytics solution by combining the best SQL technologies used in data warehousing, Azure Data Explorer for time and log series analytics, and Apache Spark technologies for big data. Additionally, Synapse can ingest, manage, prepare, and serve the data for immediate business intelligence and machine learning needs.
Key features of Azure Synapse Analytics:
- Multiple Solutions: Synapse has deep integrations with a wide range of Microsoft Azure technologies, including Azure Data Explorer, Azure SQL Database, Azure Data Factory, Synapse Workspace, Synapse Studio, Synapse SQL, and CosmosDB. Therefore, you get solutions for almost all your data management needs in one place.
- SQL and Spark Engine: Azure Synapse provides two types of engines for data processing: SQL and Spark. SQL uses a massively parallel processing system that runs queries over large relational and semi-structured datasets. However, Spark is a distributed engine that handles streaming, batch, and machine learning workloads.
- Data Exploration and Visualization: With Synapse, you can use many tools and frameworks to explore and visualize data. For instance, Azure Synapse Notebooks is an interactive tool to write and run code in Python, Scala, or .Net languages. You can also use Azure Synapse SQL Serverless to run ad-hoc queries over data lake files.
Azure Synapse Analytics has six pricing editions, which range from $4,700 monthly for 5,000 synapse commit units to $259,200 for 3,60,000 synapse commit units.
7. Best Cloud Data Services: Power BI (BI and Data Visualization)
As an organization, your data might be stored in an Excel sheet or a cloud-based and on-premises data warehouse. However, what is the point of storing and managing data if you can’t extract insights? That’s where Power BI comes in. Power BI is a collection of software services, connectors, and applications to convert raw, unrelated data into visually immersive, coherent, and interactive insights. It lets you easily connect with various data sources, discover and visualize what’s important in data, and share it with anyone you want.
Key features of Power BI include:
- Data Analysis Expressions (DAX): DAX is a library of functions and operators in Power BI that can be combined to build expressions and formulas. With its pre-built functions, it enables you to craft complex measures, calculations, and data models within the platform efficiently.
- Accessibility: You can access large amounts of data from more than 100 data sources using Power BI. It allows you to analyze, view, and visualize vast quantities of data that Excel cannot open. Some well-known data sources available for Power BI are CSV, XML, JSON, PDF, etc.
Power BI offers three pricing options: Free, Pro, and Premium. The Power BI Pro costs $10/ month, and the Premium costs $20/ month per user.
8. Best Cloud Data Services: Tableau (BI)
Like Power BI, Tableau is a business intelligence and data visualization tool used for reporting and analyzing vast volumes of data. As a user, you can create charts, dashboards, graphs, maps, and many more visualizations to make the most of existing data and make better business decisions. You can also use Tableau’s intuitive drag-and-drop user interface to extract different insights, visualize any data, and even combine databases easily.
Key features of Tableau are:
- In-Memory Data Connection: Tableau offers in-memory data connection to external and live data sources. Creating a live data connection allows you to consume data straight from the source. This ensures you combine data from multiple data sources freely.
- Trend Lines and Predictive Analysis: With Tableau’s intuitive user interface and robust back end, creating trend lines and forecasting is easy. Just pick particular parameters and perform drag-and-drop operations in your concerned field to acquire data prediction.
- Ask Data Tool: The ask data tool has increased the popularity of Tableau around the globe. This tool allows you to use your natural language to interact with data through an intuitive interface. You ask a question with a guided search suggestion to the tool, and it responds with rich data visualization results to get the insights you need from your data.
Tableau has three pricing plans: Viewer, Explorer, and Creator. The viewer plan costs $15/month, Explorer costs $42/month, and Creator costs $75/month per user.
9. Best Cloud Data Services: Databricks (AI and Analytics)
Databricks is a unified analytics platform for building, sharing, deploying, and maintaining enterprise-grade data and AI solutions. Its solutions include cloud data migration services, data workflow management, visualization generation, security management, data discovery, machine learning, and AI. Using these solutions, you can connect your data sources to a single platform to store, process, share, analyze, model, and monetize data. Additionally, Databricks can integrate with cloud storage. Therefore, you can use it to manage and deploy cloud infrastructure.
Key features of Databricks:
- Data Source Connectivity: You can connect with various on-premise and cloud data sources to perform services like Big Data Analytics using Databricks. This includes AWS, Azure, Google Cloud, SQL servers, JSON, and CSV. Additionally, it extends connectivity to Avro, MongoDB, and many other files.
- Apache Spark Integration: Databricks is built on top of Apache Spark, an open-source unified analytics engine for data processing. Using this connectivity, you can leverage the analytical capabilities of Spark in Databricks.
- Notebook Interface: Databricks offers a notebook interface supporting multiple languages in the same environment. Using this tool, a developer familiar with Python, Scala, SQL, or R can build algorithms to streamline data management tasks. For instance, data transformation tasks can be performed using SQL, model performance can be evaluated using Python, and Scala can make model predictions.
Databricks uses a pay-as-go pricing model, which only costs as per the use of services and products.
10. Best Cloud Data Services: DataRobot (Machine Learning and AI)
DataRobot is a machine learning tool for automating, accelerating, and assuring predictive data analytics. This helps data scientists and analysts build and manage accurate models in a fraction of the time other solutions require. DataRobot streamlines the work of seasoned data scientists by providing an extensive library of algorithms and new data scientists by eliminating the need for trial and error. Most importantly, it reduces the cost, risk, and time of adapting predictive analytics for better business decisions.
Key features of DataRobot:
- Automated Machine Learning: DataRobot automates machine learning workflow at several stages, including data preparation, feature engineering, hyperparameter tuning, and model evaluation. This feature reduces the time and effort needed to create precise machine-learning models.
- Collaboration and Governance: DataRobot is mainly designed for collaboration, which allows multiple professionals to work on the same project simultaneously. To streamline working in this collaborative environment, it provides many features for tracking changes, managing model versions, and implementing governance policies.
DataRobot is available for an annual subscription. You can customize the platform’s solutions pricing to meet the functionality you need.
Whether it is storing, processing, handling, or managing the data, the cloud data services tools we have discussed in this article are leading in different aspects of data management, catering to the diverse needs of modern businesses. To harness the full potential of data assets, you should stay updated with the Cloud Data Services tools mentioned above as the technologies of tools are rapidly involving.
Learn more about Hevo
If you’re looking to integrate data on a single platform and make it analysis ready, consider using Hevo Data. With the range of readily available connectors, Hevo simplifies the data integration process; it’ll only take a few minutes to set up an integration and get started.
Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. Also check out the Hevo pricing to choose the best plan for your organization.
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