What are the top cloud-based data storage solutions for data scientists?

You know, object storage has been very useful for data scientists. It is super handy for handling all sorts of data—whether it's images, videos, or documents, etc. The thing is, services like Amazon S3, Google Storage, and Azure Blob Storage offer this scalable solution. And the best part is It's cost-effective too. So, when I need to store huge amounts of unstructured data for my analytics or data projects, object storage is choice. It's like having this giant digital warehouse where we can easily stash and access all data whenever we need it.

Object storage is a computer data storage architecture designed to handle large amounts of unstructured data. Data is stored in a flat address space called a storage pool, rather than in a hierarchical file-folder structure Objects can be distributed across multiple physical storage devices Metadata is used to describe and provide context about the data, enabling advanced data management and analytics Access is typically via HTTP-based REST APIs Object storage offers advantages like scalability, cost-effectiveness for large data volumes, and flexibility for unstructured data. It is commonly used for use cases like backup, archiving, media storage, big data analytics, and IoT data.

Data scientists need to process large amounts of data and one of the most optimal and cheapest ways available on the market is object storage and it is important to know that not only large providers such as Amazon, Google and Microsoft offer these services for example, there are also Alibaba cloud, Huawei cloud, Linode, among others.

Los científicos de datos necesitan procesar gran cantidad de datos y una de las formas mas optimas y baratas disponibles en el mercado es el almacenamiento de objetos y es importante saber que no solo los grandes proveedores como Amazon, Google y Microsoft ofrecen estos servicios por ejemplo también están : Alibaba cloud, Huawei cloud, Linode, entre otros.

Top cloud-based data storage solutions for data scientists include: 1. Amazon S3 (Simple Storage Service) 2. Google Cloud Storage 3. Microsoft Azure Blob Storage 4. IBM Cloud Object Storage 5. Snowflake 6. Databricks Delta Lake 7. Amazon Redshift 8. Google BigQuery These platforms offer scalable, secure, and reliable storage for large volumes of data, along with features like data warehousing, analytics, and integration with other cloud services.

Data lake is one of the good options to support any data analytics and data engineering needs. The underline data may stored as object, file or block irrespective of its storage types it is accessed as a centralised catalog which helps data scientists get all the information at one place.

By my experience some strategic advantages are: enabling more sophisticated and varied analyses, data lakes empower data scientists to innovate, developing new products, services, and insights that can provide a competitive advantage. Agility: The ability to quickly ingest, store, and analyze new data sources allows organizations to be more agile in responding to market changes and emerging trends. Data-Driven Insights: The comprehensive data analysis enabled by data lakes leads to more informed decision-making, driving business strategies and operations with data-backed insights. Leveraging predictive analytics helps organizations anticipate and prepare for future events, leading to proactive rather than reactive strategies.

Advanced analytics, data warehousing, and AI workloads. Azure Data Lake Storage Scalable, high-performance storage for big data analytics. Integration with Azure Synapse Analytics, Azure Databricks, and other Azure services. Fine-grained access control and encryption. Data lakes provide data scientists with a powerful and flexible platform to store, process, and analyze vast amounts of data from diverse sources. By leveraging data lakes, data scientists can perform complex analytics and machine learning tasks efficiently, without the constraints of traditional data storage systems.

I usually recommend this option since is more general, allows you to work with baseline non structured data and provides flexibility and cost management options, not just in aws s3 but also in Azure and gcp. This also allows for data models in different stages, hence allows for early stages try outs

When processing high volumes of data most for Data Engineering. A very efficient Block Storage is necessary. A Block Storage with high IOPS (input/output operations per second). This improves the speed of performing a transformation of the data and writing it back to the storage system. There are various types of Block storage systems. But as a data scientist, it is essential to pick the most efficient Block Storage system that will work for your data science workload to avoid latency and performance issues. A Block storage with the SSD format are the best to choose from when setting up a Block storage service in the Cloud

In simple terms, the block storage is similar to traditional hard drives or SSDs. We can take advantage of high performance, scalability, high availability, reliability by using cloud block storages. Here are some use cases: 1. To store Databases. 2. Attach with VMs. 3. Big data and Analytics. 4. ML model training 5. Data processing and transformation. Different cloud providers are offering varieties of block storage solutions. AWS is offering EBS service which supports SDDs (General purpose / Provisioned IOPS) and HDDs (Throughput optimized / Cold HDD). Azure is also offering block storage which supports HDDs and SSDs (Standard / Premium / Premium v2) and Ultra disks. We should choose the performance tiers based on our requirements/budget.

Block storage is also important to optimize the tracking of the history of modifications of binary files through incremental changes. Only modified blocks must be saved, but unchanged blocks can be shared by all file versions, saving the storage space.

File storage in the cloud is akin to a well-organized digital library. Just like a library, it provides a familiar, structured way to store and share files, making collaboration seamless. This is particularly valuable for data scientists working with shared scripts, intermediate datasets, and results. Cloud-based file storage solutions like Amazon EFS, Azure Files, and Google Cloud Filestore offer scalable and reliable access to data, supporting both individual and team projects. Example use cases include storing Jupyter notebooks, managing large datasets, and enabling collaborative data analysis. Their ease of use and integration with various tools help streamline workflows, making them indispensable in data science.

For data scientists working with confidential or regulated data, hybrid storage solutions represent a strategic approach to data management. By combining the security and control of on-premises storage with the scalability and flexibility of the cloud, hybrid solutions ensure that sensitive data is protected while enabling the advanced data analytics capabilities necessary for innovation and growth. As data continues to be a pivotal asset, hybrid solutions will likely become an integral part of the data scientist’s toolkit, driving the future of secure and efficient data science practices.

Each of these platforms has unique features and pricing models, so the best choice depends on the specific needs of your project, such as the type of data you're handling, the required access speed, scalability needs, and your existing cloud environment. 1- Amazon S3 (Simple Storage Service) 2- Google Cloud Storage 3- IBM Cloud Object Storage 4- Snowflake As I’m Microsoft architect I’m prefer the Microsoft Azure Blob Storage: Offers a robust and scalable object storage solution for the cloud. It's particularly useful for storing large volumes of unstructured data, such as text or binary data, which is common in data science.

Multi cloud is very common solution nowadays. Many cloud vendors also allow Zero-ETL integrations minimising the effort to connect data from cloud to another. It’s very common to see architecture that is split between Azure, AWS, Google Cloud, Oracle OCI on infrastructure side and Salesforce, SAP, ServiceNow, Workday…etc on applications side. There are also common (especially in industries with a lot legacy systems like banking) to have IBM OpenShift and similar solutions. Popular are also platforms like Databricks and Snowflake. Latest addition is Oracle GoldenGate 23ai which brings multi cloud capabilities for real-time data replication between multiple clouds including vectors. There are also cloud-interconnect for example Azure / OCI.

I see multi-cloud strategies as essential for modern data storage solutions. Leveraging multiple cloud providers offers flexibility, resilience, and cost optimization. This strategy is invaluable for data scientists who need to avoid vendor lock-in, optimize costs, and ensure high availability. Being cloud-agnostic allows data scientists to use the best tools and services from each provider, enhancing productivity and innovation. For example, storing sensitive data on one provider while using another for intensive data processing can optimize performance and costs. Solutions like Google Anthos, AWS Outposts, and Azure Arc facilitate seamless integration across platforms, enabling robust and versatile data science workflows.

Data Lakehouse platform Data lakes have been of tremendous use within the data ecosystem making it easy to mange structure and unstructured data in a data format. Apache Hudi, Iceberg and Delta lake are the common lakehouse platforms with Apache X table acting as translation and unifying layer among all open data lake tables.

Data Warehouse - example BigQuery on Google Cloud. With similar feel to classic SQL databases but optimized for storing large structured data, querying it, and building Big Data models using extended SQL commands.