Data Engineering
Calgary OSIsoft PI Experts and Calgary OSIsoft AF Experts. Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. Data engineering is used in just about any industry. Data engineers build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. The key objective is to make data available so that organizations can use it to evaluate and optimize their performance.
A number of tools and technologies are used in data engineering. To start off the process, data must be collected. Tools that aid with collection include ETL applications, streaming applications, and IoT devices for instance. The protocols to collect this data are varied, but from a cloud data ingestion standpoint, AMQP and MQTT are common. The data is then persisted to a variety of data stores including databases, data lakes, data warehouses, and more recently lakehouse architectures. Analytical tools are then used to cleanse, organize, and augment the data so that it is in a usable state for analytics and visualization needs. Several of these tools are open-source, while others are closed platform or cloud-based.
We at MetaFactor have been helping customers over many years to help make their data accessible from an operational business standpoint. In recent years, we have been helping customers in the area of data engineering. With the democratization of powerful analytical tools and AI frameworks, customers have been seeking ways to get their data into these other tools and frameworks. We have helped customers build robust data pipelines to ensure that their analytical and visualization needs are met.
Open Source Toolsets
These are the most common and popular open-source toolsets to aid data engineering efforts.
Python
Python is one of the most popular programming languages. Python has a simple and easy to understand syntax. Additionally, it has plenty of libraries that serve a numerous use cases in the field of Data Engineering, Data Science, and Artificial Intelligence. Popular example libraries include Pandas, NumPy, SciPy, among many others.
Apache Spark
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. It supports the ingestion of batch and streaming data, SQL analytics, and data science and machine learning functions in several languages including Python, SQL, Scala, Java or R.
Apache Kafka
Apache Kafka is a distributed event store and stream-processing platform. It is written in Java and Scala. It allows publish-subscribe capabilities and can store streams of data reliably and durably. Client applications to process event streams in parallel at scale can be written using high level APIs in numerous languages or REST APIs.
Cloud-Based Toolsets
Here are some of the most commonly used tools for data engineering from the Microsoft Azure or Amazon AWS platform. These two platforms are the leading cloud providers and have a number of services that can be used to facilitate data engineering functions. We are listing some of the most popular features and services here.
Azure Synapse
Azure Synapse is Microsoft's cloud-based analytics and lakehouse service that brings together data integration, enterprise data warehousing and big data analytics. It enables direct query from the Azure Data Lake or SQL data warehouse using SQL or Spark-based clusters. Synapse has built-in ETL pipeline features as well, and also supports access to files in Delta Lake format.
Databricks
Databricks is a managed Spark offering, optimized for various cloud service providers including Azure, AWS, and GCP. It is integrated with cloud data lake and ETL services, as well as machine learning and data warehousing services. Databricks brings open-source technologies such as Apache Spark or Delta onto a single unified platform, improves them, and hardens them so they are enterprise ready out of the box.
Amazon Redshift
Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and machine learning to deliver high performance. Similar to Azure Synapse, it is supported by an ecosystem of connectors, auto-scaling features, and analytical toolsets such as Amazon QuickSight to enable operational insights.
How Can We Help?
The section below outlines a number of ways in which we can help. We have data engineering specialists who can help with a diverse array of needs. If your need or scenario isn't covered here, contact us anyway and we can discuss ways in which we can help you.
Build Analytical Pipelines
We will help build data pipelines using ETL / ELT solutions, big data processing frameworks, and machine learning notebooks. With our in-depth knowledge in connecting to data historian frameworks, we can accelerate your data integration and analytics efforts as well.
Analytical Data Access
We will help you access your analytics-enriched data from the cloud or other framework and integrate this data with your other business applications. This may mean access from analytical tools like Power BI or embedding the data in other applications. Or it may mean productionizing machine learning models.
Architect Solutions
We will assess your analytical needs and help produce scalable and robust architectures that meet your needs. Consistency models, storage frameworks, and ingestion and analytical frameworks will all be fit for your needs. We have an informed perspective when it comes to the challenges involving operational data.