Data sources ingest data in different sizes and shapes across on-premises and in the cloud, including product data, historical customer behaviour data, and user data. Enterprise could store these data in data storage services like Azure Blob store, an on-premises SQL Server, Azure SQL Database, and many more.
This blog will highlight how users can define pipelines to migrate the unstructured data from different data stores to structured data using the Azure ETL tool, Azure Data Factory.
Before diving deep into Azure Data Factory, there is a need to know what the ETL tool is all about. ETL stands for Extract, Transform and Load. The ETL Tool will extract the data from different sources, **transform **them into meaningful data and **load **them into the destination, say Data warehouses, databases, etc.
To understand the ETL tool in real-time, let us consider management with various departments like HR, CRM, Accounting, Operations, Delivery Managements, and more. Every department will have its datastore of different types. For instance, the CRM department can produce customer information; the Accounting team may keep various books, and their Applications could store transaction information in Databases. The organization needs to transform these data into meaningful and analyzable insights for better growth. Here comes the ETL tool like Azure Data Factory. Using Azure Data Factory, the user will define the data sets, create pipelines to transform the data and map them with various destinations.
As cloud adoption keeps increasing, there is a need for a reliable ETL tool in the cloud with many integrations. Unlike any other ETL tools, Azure Data Factory is a highly scalable, increased agility, and cost-effective solution that provides code-free ETL as a service. Azure Data Factory consists of various components like:
**Pipelines: **A pipeline is a logical grouping of activities that performs a unit of work. A single pipeline can perform different actions like ingesting data from the Storage Blob, Query the SQL Database, and more.
**Activities: **Activity in a Pipeline represents a unit of work. An Activity is an action like copying a Storage Blob data to a Storage Table or transform JSON data in a Storage Blob into SQL Table records.
**Datasets: **Datasets represent data structures within the data stores, which point to the data that the activities need to use as inputs or outputs.
Triggers: Triggers are a way to execute a pipeline run. Triggers determine when a pipeline execution should start. Currently, Data Factory supports three types of triggers:
Schedule Trigger: A trigger that invokes a pipeline at a scheduled time.
Tumbling window trigger: A trigger that operates on a periodic interval.
Event-based trigger: A trigger that invokes a pipeline when there is an event.
Integration Runtime: The Integration Runtime (IR) is the compute infrastructure used to provide data integration capabilities like Data Flow, Data Movement, Activity dispatch, and SSIS package execution. There are three types of Integration Runtimes available, they are:
Now let us see how to migrate the unstructured data from the Storage Blob into structured data using Azure Data Factory with a real-time scenario.
Consider a developer should design a system to migrate the CSV file generated from the CRM Application to the central repository, say, Azure SQL Database for automation and analytics. CSV file contains the unstructured data of more than 1000 customer records with a delimiter. These records should be efficiently migrated to the Azure SQL Database, which is a central repository. Here comes the Azure Data Factory. It allows creating a pipeline to copy the customer detail records from CSV to the CustomerDetails Table in Azure SQL Database.
Following are the steps to migrate data from CSV to Azure SQL Database:
1. Create an Azure Data Factory and open the Azure Data Factory Editor
2. Now go to the Editor page and Click the + button to create an Azure Data Factory pipeline
3. Provide the Name of the Pipeline (Migrate_Customer_Details) as shown below
#microsoft-azure #aws #data #data-science #sql
Databases are the lifeblood of every business in the modern world. Data enables them to make informed and valuable decisions. Insights, patterns, and outcomes – all require the best quality of data. Therefore, when it comes time to move from an older version to a newer version of the software, there’s a need for data migration planning.
There are a lot of complexities involved in the data migration process. You can’t just copy and paste data – it’s much more complicated. You need to have some data migration strategies and best practices for the entire process. You have to create a data migration plan that outlines all the activities of the process.
Data migration takes anywhere between a couple of months to a year. It depends on the amount of data you have, the capabilities of your legacy system, and the compatibility with the new system. While there are data migration tools and software that make the work easier, you need to have a data migration checklist for beginning the procedure.
In this article, we will look at the different data migrations strategies that assist in better managing data while moving from legacy systems or upgrading. We hope that your data migration team will get an overview of the process and the best practices that they can adopt.
The primary purpose of a data migration plan is to improve the performance of the entire process and offer a structured approach to your data. Data migration policies are useful to eliminate errors and redundancies that might occur during the process.
Here’s why understanding data migration strategies is important –
There are many important elements to a data migration strategy. They are critical because leaving even a single factor behind may impact the effectiveness of your strategy. Your data migration planning checklist can comprise of the following –
Now that you have a clear understanding of why a data migration strategy is needed and what it comprises, let’s move on to the best data migration strategies and best practices.
As you go through the process of data migration services, understanding how the process works is an essential step. Most data is migrated when there is a system upgrade. However, it involves a lot of challenges that can be solved easily by following the best practices.
We learned the different data migration strategies that can enhance the performance of the migration process. Once the data is lost, recovering it is more of a hassle than migrating it. So to ensure that you have the right assistance in data migration, hire the experts from BoTree Technologies. Call us today!
#data #data migration strategy #data migration #data migrations strategies #data migration software #data migration services
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.
To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.
The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:
#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics
In today’s tech world, data is everything. As the focus on data grows, it keeps multiplying by leaps and bounds each day. If earlier mounds of data were talked about in kilobytes and megabytes, today terabytes have become the base unit for organizational data. This coming in of big data has transformed paradigms of data storage, processing, and analytics.
Instead of only gathering and storing information that can offer crucial insights to meet short-term goals, an increasing number of enterprises are storing much larger amounts of data gathered from multiple resources across business processes. However, all this data is meaningless on its own. It can add value only when it is processed and analyzed the right way to draw point insights that can improve decision-making.
Processing and analyzing big data is not an easy task. If not handled correctly, big data can turn into an obstacle rather than an effective solution for businesses. Effective handling of big data management requires to use of tools that can steer you toward tangible, substantial results. For that, you need a set of great big data tools that will not only solve this problem but also help you in producing substantial results.
Data storage tools, warehouses, and data lakes all play a crucial role in helping companies store and sort vast amounts of information. However, the true power of big data lies in its analytics. There are a host of big data tools in the market today to aid a business’ journey from gathering data to storing, processing, analyzing, and reporting it. Let’s take a closer look at some of the top big data tools that can help you inch closer to your goal of establishing data-driven decision-making and workflow processes.
#big data #big data tools #big data management #big data tool #top 10 big data tools for 2021! #top-big-data-tool
This 7-step data migration plan will help ensure your data will be safe, sound, and smoothly transferred wherever you need it to.
Data migration is complex and risky — yet unavoidable for most companies’ processes. Especially now, at times of mass transitioning from on-premises systems to the cloud, companies are migrating their data to or in-between Microsoft, Google, or AWS cloud storage.
Regardless of the reasoning behind your data migration, the process and its pitfalls stay the same: downtime, data misplacement, data corruptions, losses, leaks, format incompatibilities, etc. In fact, Bloor’s data migration report shows that 84% of data migration projects overrun time or budget and 70-90% of migrations don’t meet expectations.
Of course, the severity of failed migration consequences varies depending on the company’s size, the volume and importance of data, compliance implications, and more. But no matter if you are a small-to-medium or enterprise-sized company, losing data and money due to poor migration will take its toll one way or another.
To help you avoid this scenario, we prepared a 7-step data migration plan to help ensure your data will be safe, sound, and smoothly transferred wherever you need it to. These rules apply to every type of data migration, but if you’re interested in migrating Google data specifically, read this article.
#cloud #big data #data migration #data migration automation #data migration best practices #g suite data migration