Ideal Cloud-based Data Lake Framework

We know that with the right technology, we can do much better than just keep up and if we could also ensure flexible development and make it easier to protect our data, process to access, process and analyze data whenever it’s required. With the right tools and best practices, an organization can use all its data, making it accessible to more users and fueling better business decisions.

New technologies innovations can improve improve the modern cloud-based data lakes, data warehousing and analytics with regard to availability, simplicity, cost, and performance which should be meet current and future needs by ableing to scale both compute and storage independently. It shouldn’t interfere with any ongoing workloads, degrade performance, or result in service unavailability due to backup processes running in the background. And it should be cheap, with clever ways to preserve our data without having to copy and move it somewhere else.

Technology is the most basic need for Any Modern Data Lakes — Now a days, many technologies such as Databricks, Microsoft Azure, AWS cloud are providing many services to support Big data which is an idea as much as a particular methodology to enabling powerful insights, faster and better decisions, and even business transformations across many industries.

So, we focus on the key technologies that should be part of any modern data lakes for supporting big data means any kind of the data.

  1. Cloud comes with Unlimited Resource— Cloud based services are particularly well-suited for data lakes because it gives us unlimited resources means cloud infrastructure delivers near-unlimited resources, on demand, and within minutes or seconds without worrying any thing. Organizations pay only for what they use, making it possible to dynamically support any scale of users and workloads without compromising performance.
  2. Cloud technology to Save money, focus on data — Cloud based services provide a cloud-built solution to any organization to avoid the costly, up-front investment of hardware, software, and other infrastructure, and the costs of maintaining, updating, and securing an on-premises system.
  3. Cloud technology comes with Natural Integration Point: By some estimates, as much as 80% of data you want to analyze comes from line of business app data, operational data store, click stream data, social media plateform, IoT things and Real time Streaming data. Bringing that data together in the cloud is dramatically easier and cheaper than building an internal data center.
  4. In-built with noSQL — It describes a technology that enables storing and analyzing newer forms of data, such as data generated from machines and from social media, to enrich and expand an organization’s data analytics. As we know that traditional data warehouses don’t accommodate these data types very well. Therefore, newer systems have emerged in recent years to handle these semi-structured and non-structured data forms such as JSON, Avro, and XML.
  5. Supports existing skills and Expertise — Data lake supports the capabilities needed to effectively store and process any type of data, data management, data transformation, integration, visualization, business intelligence, and analytics tools that can easily communicate with a SQL data warehouse. The well-established role of standard SQL also means a huge number of people have SQL skills. It enables other programming languages to extract and analyze data.

Selecting the Best Cloud-based Data Lake Ecosystem — The ideal cloud data lake solution delivers the best of both worlds — the flexibility to integrate relational and nonrelational data along with identify services to make the desired architecture approach possible and practical for the enterprise, business users, and data scientists alike. The best cloud-based data lake ecosystem offerings illustrate the points perfectly. These include:

  1. Storage — Data lake storage needs to be capable of holding extreme amounts of structured, semi-structured and unstructured data. While Hadoop’s HDFS is capable, cloud-based object storage may be a better choice for data redundancy distributed not only across nodes. AWS offers both Amazon Simple Storage Service (S3) for reliable, secure, and scalable object storage and Amazon Glacier, which has similar qualities for extremely low-cost, long-term archiving and backup with minimal administrative overhead.
  2. Compute — In data lake, you can easily apply different analytics algorithms by using different compute resources. For example, streaming analytics will need high throughput, while batch may be processor-intensive. Apache Spark can require a lot of memory, while AI may work best on GPUs. An ideal cloud based data lake services offers significant flexibility compared to other cloud providers as well as on-premises Hadoop, which ties storage directly to compute in each node.
  3. Analytics — A data lake virtue is how it enables the same data to be analyzed in many different ways for many different use cases. An ideal cloud based data lake ecosystem no need for data migration to different operating environments, or the accompanying overhead, costs, effort, or delays.
  4. Databases — Not all data lake data is unstructured. Often, it makes sense to have tighter organization for both transaction and analytics processing. Again, this provides the versatility to meet the needs of many data lake applications.
  5. Real-time streaming processing — Not all data is simply stored in the data lake and analyzed later. Often, there is a need to collect, store, process, and even analyze real-time data in motion. An ideal cloud based data lake ecosystem offering powerful services to collect, store, and analyze streaming data as well as the ability to build custom streaming data applications for specialized needs.
  6. Artificial intelligence —This is the most useful feature of any ideal cloud based data lake ecosystem. Increasingly, artificial intelligence and machine learning are becoming popular tools for building smart applications such as predictive analytics and deep learning.
  7. Security services — As shown, security, privacy, and governance are essential elements for sensitive data to be trusted to a cloud data lake.
  8. Data management services — As data is used in different platforms, ETL is an important function to ensure that it is moved and understood properly. An ideal cloud based data lake ecosystem must have an ETL engine to easily understand data sources, prepare the data, and load it reliably to data stores.
  9. Application services — While the data lake can be an invaluable resource in its own right, it really comes alive when integrated with higher-level applications. An ideal cloud based data lake ecosystem has fully capable utilities for IoT use cases, for mobile applications, and for API calls to anything else.

A basic premise of the data lake is adaptability to a wide range of analytics and analytics-oriented applications and users and all the additional enterprise needs are covered with services like security, access control, and compliance frameworks and utilities.

Reference — Databricks, AWS, Microsoft Azure and Snowflake

BI Specialist || Azure || AWS || GCP — SQL|Python|R|PySpark — Talend, Alteryx, SSIS — PowerBI, SSRS expert at The Smart Cube

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