Understanding the Basics of Data Lakes
Learn how to harness the power of data engineering and analytics with data lakes. Discover the value of data lakes, how they differ from data warehouses, and whether you should take a build or buy approach.
Data drives today’s businesses and economies. In the ever-competitive economy, organizations need to make strategic decisions which have a reliable foundation on data. High performance artificial intelligence (AI) models trained by data provide that needed insight.
The Internet of things (IoT) network, made up of connected sensors, controllers, microchips, etc, has enabled applications to reach almost everywhere by data transmission on the Internet. However, refining this data to use it to its advantage requires more than just hiring data scientists, which is one of the reasons that 85 percent of big data projects fail. You need to make sure you have a data strategy with executive buy in.
Using this data to drive an organization forward requires having the right tools to provide valuable insights and a solid data infrastructure to support all an organizations current and future analytical needs. Data infrastructure should collect, store, structure, organize, and maintain all and only the data an organization needs.
Determining how to build data infrastructure on AWS is an essential decision since it will power an organization’s data engineering, analytics, and machine learning projects.
Data infrastructure must have policies and guides to structure the data to facilitate high-quality insights for decision-makers. A traditional infrastructure collects data from various sources. Then it cleans and pre-processes it. To analyze this data, companies can convert it into query analysis using business intelligence (BI) tools. The next step is extracting, transforming, and loading (ETL) data, usually into data warehouses. Finally, companies must regularly audit this data.
One problem with the traditional data infrastructure is its limited ability to scale up. The result is communication bottlenecks, delayed reports, and analytics hindering timely data for informed decision making. Since today’s organizations must manage heterogeneous data of massive volumes, they need more reliable and agile storage and analytics solutions. Moreover, different situations call for other tools.
Amazon Web Services (AWS) offers various options depending on the situation and an organization’s needs. AWS provides flexibility depending on the data maturity level of an organization.
Data import and ingestion are the foundation of AWS infrastructure. Organizations of all kinds transport data from one or more sources, ingest it, and store it for further analysis. This process identifies data sources, validates them, and routes the data. To build a data infrastructure, companies must consider the kinds of data sources they are using and whether the data is unstructured, semi-structured, or fully structured.
Data can arrive from many sources:
So, one of the first decisions a company must make is which tool to use for this process. Suppose a company is importing and ingesting data from the IoT. In that case, a suitable tool is Amazon Kinesis, which enables companies to ingest, process, and analyze real-time streaming data such as video and audio, IoT telemetry, and application logs for analytics or machine learning applications.
Many companies want to move data from local disks to cloud storage to take advantage of the cloud’s durable, resilient, cost-effective, and secure storage services. And, for companies using machine learning (ML), it’s easier to apply ML and analytics tools in the cloud. If a company wants to migrate, transport, and analyze data in remote locations, a tool such as Amazon Snowball might be a good fit since it is scalable, secure, and offers stand-alone storage.
Some companies require massive storage and a hybrid infrastructure that stores some data on a private network and some on the cloud. Tools like AWS Storage Gateway offer simplified, virtually unlimited, hybrid cloud storage. Companies can connect applications to the service through a virtual machine or gateway hardware appliance using standard storage protocols. Also, they can cache data locally for low-latency access. This is especially useful in scenarios when companies want to move backups to the cloud or provide low latency access to data in AWS applications.
After storing data in a data lake, organizations need rules governing data access and use. Gone are the days when organizations had to choose between innovation and control. AWS offers companies both — and enables them to provision and operate its environment with agility.
For example, AWS Management and Governance services offer a simple single control plane to manage AWS resources, no matter the scale. A tool such as AWS CloudFormation enables companies to model, manage, and provision a collection of related AWS resources in multiple AWS accounts and regions. Companies can model resources and their dependencies in a CloudFormation template.
AWS Management and Governance services also govern the use of those resources and identify anyways to reduce the cost. For example, DevOps, site reliability engineers, or managers may want a tool like Amazon CloudWatch. Amazon CloudWatch is a monitoring and observability service that gives companies a consolidated view of operational health and the robustness of any deployed AWS resources and applications. This helps companies understand and optimize resource utilization and respond to performance changes.
Moreover, there are AWS tools to capture data flow from the very source to the destination. These tools provide companies with the ability to ensure that the data it is using is from trusted and authorized sources. AWS Config, for example, provides continuous monitoring and recording of AWS resource configuration and allows companies to automate the comparison of these recorded configurations to desired ones. It’s a fully managed service that provides AWS resource inventory, configuration history, and change management.
In the case of companies that have just provisioned AWS infrastructure or already have an AWS infrastructure, a tool like AWS Managed Services can augment and optimize the operational capabilities of newly provisioned or existing AWS infrastructure.
Visit Management and Government on AWS for other governance services that AWS provides.
A data pipeline is an end-to-end process to ingest, process, prepare, transform, and enrich structured, unstructured, and semi-structured data in a governed manner. As enterprises build, consolidate, or modernize analytics in the cloud, they need an intelligent, automated cloud-native data pipeline to process raw data coming in from applications, legacy systems, social media, devices, IoT sensors, and more.
AWS services, such as Glue, offer a complete pipeline for automating the processing, transformation, and movement of data from various sources across a data infrastructure.
As a managed service, Glue provides a scalable data pipeline platform that allows data discovery through a Glue crawler, extract, transform, and loading of the data (ETL) through Glue jobs, and automation of the entire pipeline via Glue Workflows. All the data is managed through a centralized data catalog that is integrated across the AWS data ecosystem. Moreover, the entire pipeline can be managed without the need to write complex ETL code and can even be performed visually through Glue Studio or Databrew.
Organizations will never be able to use 100 percent of their data they collected, but if they work hard and properly analyze their data, even small amounts will lead them to make reliable decisions. Whatever tool or software a company uses, it should provide insights for critical decisions when necessary.
The volume of information collected these days means higher costs. So, companies must make sure that it is useful. Also, the data must be accurate. and it must be valuable. Traditional BI tools cannot provide the data granularity need to make these evaluations. Moreover, they are static, and their approach is reactive rather than proactive.
Tools like Amazon Quick Sight enable companies to create and publish modern, interactive, and machine learning-powered BI dashboards.
With the right data infrastructure and a proper set of tools, high volumes of data can help optimize a business. AWS has many powerful tools and services, but choosing from hundreds of offerings can be daunting. In such a case, it is wise to opt for a cloud service provider, like Mission that offers professional and managed services.
With Mission, you can harness the full power of AWS. Whether you are introducing a new product or looking to transform an existing business, Mission helps you design, build, migrate, and manage scalable technology solutions. Mission offers well-architected monitoring for complete optimization and governance of AWS resources.
If your organization is looking for a serverless consultation or moving towards containerized architecture or DevOps management, Mission can help you achieve your goals. Learn more or get in touch with our experts.