Automating Intelligent ETL with Data Live Tables

Given the fast-paced changes in the market environment and the need to retain competitive advantage, businesses must address the challenges, improve efficiencies, and deliver high-quality data reliably and on time. This is possible only by automating ETL processes.

The Databricks Lakehouse Platform offers Delta Live Tables (DLT), a new cloud-native managed service that facilitates the development, testing, and operationalization of data pipelines at scale, using a reliable ETL framework. DLT simplifies the development and management of ETL with:

• Declarative pipeline development
• Automatic data testing
• Monitoring and recovery with deep visibility

With Delta Live Tables, end-to-end data pipelines can be defined easily by specifying the source of the data, the logic used for transformation, and the target state of the data. It can eliminate the manual integration of siloed data processing tasks. Data engineers can also ensure data dependencies are maintained across the pipeline automatically and apply data management for reusing ETL pipelines. Incremental or complete computation for each table during batch or streaming run can be specified based on need.

Benefits of DLT

The DLT framework can help build data processing pipelines that are reliable, testable, and maintainable. Once the data engineers provide the transformation logic, DLT can orchestrate the task, manage clusters, monitor the process and data quality, and handle errors. The benefits of DLT include;

Assured Data Quality

Delta Live Tables can prevent bad data from reaching the tables by validating and checking the integrity of the data. Using predefined policies on errors such as fail, alert, drop, or quarantining data, Delta Live Tables can ensure the quality of the data to improve the outcomes of BI, machine learning, and data science. It can also provide visibility into data quality trends to understand how the data is evolving and what changes are necessary.

Improved Pipeline Visibility

DLT can monitor pipeline operations by providing tools that enable visual tracking of operational stats and data lineage. Automatic error handling and easy replay can reduce downtime and accelerate maintenance with deployment and upgrades at the click of a button.

Improve Regulatory Compliance

The event log can automatically capture information related to the table for analysis and auditing. DLT can provide visibility into the flow of data in the organization and improve regulatory compliance.

Simplify Deployment and Testing of Data Pipeline

DLT can enable data to be updated and lineage information to be captured for different copies of data using a single code base. It can also enable the same set of query definitions to be run through the development, staging, and production stages.

Simplify Operations with Unified Batch and Streaming

Build and run of batch and streaming pipelines can be centralized, and the operational complexity can be effectively minimized with controllable and automated refresh settings.

Concepts Associated with Delta Live Tables

The concepts used in DLT include:

Pipeline: A Directed Acyclic Graph that can link data sources with destination datasets

Pipeline Setting: Pipeline settings can define configurations such as;

• Notebook
• Target DB
• Running mode
• Cluster config
• Configurations (Key-Value Pairs).

Dataset: The two types of datasets DLT supports include Views and Table, which, in turn, are of two types: Live and Streaming.

Pipeline Modes: Delta Live provides two modes for development:

Development Mode: The cluster is reused to prevent restarts and disable pipeline retries for detecting and fixing errors.

Production Mode: Cluster restart for recoverable errors such as stale credentials or memory leak and execution is retried for specific errors.

Editions: DLT comes in various editions to suit the different needs of the customers such as:

• Core for streaming ingest workload
• Pro for core features + CDC, streaming ingest, and table updation based on changes to the source data
• Advanced where in addition to core and pro features, data quality constraints are also available

Delta Live Event Monitoring: Delta Live Table Pipeline event log is stored under the storage location in /system/events.

Indium for Building Reliable Data Pipelines Using DLT

Indium is a recognized data engineering company with an established practice in Databricks. We offer ibriX, an Indium Databricks AI Platform, that helps businesses become agile, improve performance, and obtain business insights efficiently and effectively.

Our team of Databricks experts works closely with customers across domains to understand their business objectives and deploy the best practices to accelerate growth and achieve the goals. With DLT, Indium can help businesses leverage data at scale to gain deeper and meaningful insights to improve decision-making.

FAQs

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Smart Data Pipelines for Ever-Changing Business Needs

The world today is moving fast, and requirements changing constantly. Businesses need to respond in real-time to improve customer delight and become efficient to become more competitive and grow quickly. In 2020, the global pandemic further compelled businesses to invest in data and database technologies to be able to source and process not just structured data but unstructured as well to maximize opportunities. Getting a unified view of historical and current data became a challenge as they moved data to the cloud while retaining a part in on-premise systems. However, this is critical to understand opportunities and weaknesses and collaborate to optimize resource utilization at low costs.

The concept of the data pipeline is not new. Traditionally, data collection, flow, and delivery happened through batch processing, where data batches were moved from origin to destination in one go or periodically based on pre-determined schedules. While this is a stable system, the data is not processed in real-time and therefore becomes dated by the time it reaches the business user.

Check this out: Multi-Cloud Data Pipelines with Striim for Real-Time Data Streaming

Stream processing enables real-time access with real-time data movement. Data is collected continuously from sources such as change streams from a database or events from sensors and messaging systems. This facilitates informed decision-making using real-time business intelligence. When intelligence is built in for abstracting details and automating the process, it becomes a smart data pipeline. This can be set up easily and operates continuously without needing any intervention.

Some of the benefits of smart data pipelines are that they are:

● Fast to build and deploy

● Fault-tolerant

● Adaptive

● Self-healing

Smart Data Pipelines Based on DataOps Principles

The smart data pipelines are built on data engineering platforms using DataOps solutions. They remove the “how” aspect of data and focus on the 3Ws of What, Who, and Where. As a result, smart data pipelines enable the smooth and unhindered flow of data without needing constant intervention or building or being restricted to a single platform.

The two greatest benefits of smart data pipelines include:

● Instant Access: Business users can access data quickly by connecting the on-premise and cloud environments using modern data architecture.

● Instant Insights: With smart data pipelines, users can access streaming data in real-time to gain actionable insights and improve decisions making.

As the smart data pipelines are built on data engineering platforms, it allows:

● Designing and deploying data pipelines within hours instead of weeks or months

● Improving change management by building resiliency to the maximum extent possible

● Adopting new platforms by pointing to them to reduce the time taken from months to minutes

Smart Data Pipeline Features

Some of the key features of smart data pipelines include:

Data Integration in Real-time: Real-time data movement and built-in connectors to move data to distinct data targets become possible due to real-time integration in smart data pipelines to improve decision-making.

Location-Agnostic: Smart Data Pipelines bridge the gap between legacy systems and modern applications, holding the modern data architecture together by acting as the glue.

Streaming Data to build Applications: Building applications become faster using smart data pipelines that provide access to streaming data with SQL to get started quickly. This helps utilize machine learning and automation to develop cutting-edge solutions.

Scalability: Smart data integration using striim or data pipelines help scale up to meet data demands, thereby lowering data costs.

Reliability: Smart data pipelines ensure zero downtime while delivering all critical workflows reliably.

Schema Evolution: The schema of all the applications evolves along with the business, ensuring keeping pace with changes to the source database. Users can specify their preferred way to handle DDL changes.

Pipeline Monitoring: Built-in dashboards and monitoring help data customers monitor the data flows in real-time, assuring data freshness every time.

Data Decentralization and Decoupling from Applications: Decentralization of data allows different groups to access the analytical data products as needed for their use cases while minimizing disruptions to impact their workflows.

Benefit from indium’s partnership with striim for your data integration requirements: REAL-TIME DATA REPLICATION FROM ORACLE ON-PREM DATABASE TO GCP

Build Your Smart Data Pipeline with Indium

Indium Software is a name to reckon with in data engineering, DataOps, and Striim technologies. Our team of experts enables customers to create ‘instant experiences’ using real-time data integration. We provide end-to-end solutions for data engineering, from replication to building smart data pipelines aligned to the expected outcomes. This helps businesses maximize profits by leveraging data quickly and in real-time. Automation accelerates processing times, thus improving the competitiveness of the companies through timely responses.

The post <strong>Why You Should Use a Smart Data Pipeline for Data Integration of High-Volume Data</strong> appeared first on Indium.

The fastest and the most efficient way to move large volumes of data is to have a standard pipeline. Big data pipelines let the data flow from the source to the destination whilst calculations and transformations are processed simultaneously. Let’s see how data migration can aid big data pipelines be more efficient:

Why Data Migration?

Data migration is a straightforward process where data is moved from one system to another. A typical data migration process includes Extract, Transform and Load (ETL). This simply means that any extracted data needs to go through a particular set of functions in preparation so it can be loaded onto a different, database or application.

There requires a proper vision and planning process before selecting the right data migration strategy. The plan should include the data sources and destinations, budget and security. Picking a data migration tool is integral to making sure that the strategy adopted is tailor-made to the organization’s business requirements or use case(s). Tracking and reporting on the quality of data is paramount to knowing exactly what tools to use to provide the right information.

Most of the times, SaaS tools do not have any kind of limitations on the operation system; hence vendors usually upgrade them to support more recent versions of both the source and destination automatically.

Having understood about data migration, let’s look at some of the desired characteristics of a big data pipeline-

Monitoring: There needs to be systemic and automatic alerts on the health of the data so potential business risks can be avoided.

Scalability: There needs to be an ability to scale up or down the amount of ingested data whilst keeping the costs low.

Efficiency: Data, human and machine learning results need to keep up with each other in terms of latency so as to effectively achieve the required business objectives

Accessibility: Data needs to be made easily understandable to data scientists through the use of query language.

Now let’s look at where data migration comes into the picture in a big data pipeline

The Different Stages of a Big Data Pipeline

A typical data pipeline comprises of five stages that is spread across the entire data engineering workflow. Those five stages in a big data pipeline are as follows:

Collection: Data sources like websites, applications, microservices and from IoT devices are used to collect the required and relevant data to be processed.

Ingestion: This step moves the streaming data and batched data from already existing repositories and data warehouses to a data lake.

Preparation: This step is where the significant part of the data migration occurs where the ETL operation takes place to shape and transform the data blobs and streams. The ready-to-be-ingested ML data is then sent to the data warehouse.

Computation: This is where most of the data science and analytics happen with the aid of machine learning. Insights and models both are stored in data warehouses after this step.

Presentation: The end results are delivered through a system of e-mails, SMSs, microservices and push notifications

Data migration in big data pipelines can take place in a couple ways depending on the business’ needs and requirements. There are two main categories of data migration strategies:

1. Big Bang Migration is done when the entire transfer is done in a limited window of time. Live systems usually go through a downtime whilst the ETL process happens. This is when the data is transitioned to a new database. There is a risk of compromised implementation, but as it is a time restricted event, it takes little time to complete.

2. Trickle Migration on the contrary, completes the migration process in different phases. During the implementation, the older and new the systems are run parallelly so as to ensure there in no downtime or operational breaks. Processes usually run in real-time that makes implementation a bit more complicated than the big bang method. But if this is done right, it reduces the risk of compromised implementation or results.

Best Practices for Data Migration

Listed down are some best practices that will help you migrate your data with desired results:

1. Backing Up Data

There are instances while migrating data that things will not always go according to plan. Things can go missing or potential data losses can occur if files get corrupted or are incomplete. Creating a backup helps to restore data to its primary state.

2. Verify Data Complexity and Standards

There arises a need to asses and check what kind of different data an organisation requires to be transferred. After finding out what the data format is and where it is stored, it can be easier to detect the quality of legacy data. This ultimately leads to being able to implement comprehensive firewalls to delineate useful data from duplicates.

3. Determine Data and Project Scope

The data migration strategy must be compliant with regulatory guidelines which means that there comes a need to specify the current and future business needs. These business rules must be cooperative with business and validation rules so as to make sure that the data is transferred consistently and efficiently.

4. Communicate and Create a Data Migration Strategy

The overall data migration process will most likely require hands-on engagement from multiple teams. Making sure there is a successful data migration strategy in check requires the team to be delegated with different tasks and responsibilities. This alongside of picking the right data migration strategy for your unique business requirements will give you the edge that you are looking for in an age of digital transformation.

Breeze through your Big Data Pipelines

Data pipelines as-a-service helps developers assembling an architecture that can help for easy upgrade of their data pipeline. There are a number of things such as being very meticulous with cataloguing that can help with bytes not being lost in transit.

Starting simple is the answer, alongside which there needs to be a careful evaluation of your business goals, the contributions to the business outcome and what kind of insights will actually turn out to be actionable.

The post Breezing through data migration for a Big Data Pipeline appeared first on Indium.

Typically, enterprises place frequently accessed data through applications, tools, and dashboards on public servers such as AWS and Azure. Sensitive or mission-critical data accessed through proprietary applications and requiring monitoring is generally kept on private servers. Depending on their use cases, they opt for multiple cloud vendors based on the services they offer.

Benefits of Multi-Cloud Pipeline

The use of data within the organization is growing in leaps and bounds, providing relevant insights to each function to enable the teams to improve their performance. However, as the same subset of data is used for different applications as input or in a different format, the data may also start getting stored on different cloud servers based on need. This leads to the formation of silos.

A multi-cloud pipeline helps to prevent the formation of silos by enabling data taken from one cloud provider to be worked on using cloud-specific tooling before loading it to a different cloud. This can take care of any compatibility issues between clouds and allow seamless access to data.

Some of the benefits of running a multi-cloud pipeline include:

Delivering different subsets of data

● To different functions

● For different applications

● In different formats

Each is likely to have different service-level requirements or specifications such as low-latency, high priority, in real-time, larger volumes, and so on. This, combined with the cloud silos, can defeat the very purpose for which an enterprise opts for cloud–to be without barriers. A multi-cloud data pipeline facilitates sharing or streaming of data over the cloud infrastructure to ensure that the multi-cloud environment delivers on its promise of providing load destinations across multiple clouds.

Building a Multi-Cloud Data Pipeline with Striim

The cloud architecture provides enterprises with tremendous cost benefits as well as increases flexibility. However, managing data across multiple locations and clouds also creates its own set of challenges and introduces the risk of cloud silo.

Traditional approaches to data movement in real-time for certain applications can become difficult due to inherent latency. With the number of sources and targets being very high, batch ETL methods also may not be able to meet the need for data movement.

This creates a need to build a streaming data pipeline for cloud services so that enterprise data can be moved in real-time from on-premises to the cloud and between cloud environments.

The Striim platform enables businesses to leverage their cloud environment for a variety of use cases by enabling the building of a streaming data pipeline. These could be for offloading of operational workloads, data center extension to the cloud, or cloud-based analytics for making informed decisions.

Advantages of Striim for Building Multi-Cloud Pipelines

Some of the advantages of using Striim for building multi-cloud pipelines include:

● Easy-to-use wizards that enable building and modifying highly reliable and scalable data pipelines that allow data to be moved continuously and in real-time without disrupting the performance of the source systems.

● Continuous data synchronization is made possible using non-intrusive, real-time change data capture (CDC) that moves and processes only changed data.

● Processing and formatting in-memory to feed data to the cloud and other targets in real-time with full context.

● In-flight aggregating, filtering, enriching, transforming, and analyzing data of the relevant data sets before delivery to the various endpoints.

● Visualize the data flow and the content of data in real-time using interactive dashboards and real-time alerts. Verify the ingestion, processing, and delivery of streaming data with the built-in data pipeline to cloud monitoring.

You might be interested in this: Cloud Data Migration Demystified

Indium–A Striim Implementation Partner

Indium Software, a cutting-edge software solution provider, is a Striim implementation partner. Our team of Striim experts with cross-domain expertise enabled a private sector bank to migrate its terabytes of data in real-time from their on-premise core banking system to the cloud without disrupting business using data pipeline. Indium leveraged Striim to create a real-time data replication pipeline from the core banking system to the target system with XML conversion on the fly. We were also able to ensure ease of use and effective data monitoring with live dashboards and a diverse set of metrics. Apart from other benefits, we were able to achieve 50% greater efficiency in data migration.

Indium’s deep expertise in implementing Striim can help businesses take advantage of its data pipeline capabilities and improve data usage for drawing meaningful insights. We also provide:

● End-to-end implementation and training in Striim

● Setting up Striim node/cluster with HSA/multi-node architecture

● Customer support for queries of POCs

● Professional services for maintenance and app development

The post Multi-Cloud Data Pipelines with Striim for Real-Time Data Streaming appeared first on Indium.

It is no more only about data processing and creating data pipelines, it is about doing it in real-time. This has created a need for technologies that can handle streaming data and enable a smooth, automated flow of information from input to output as needed by different business users. This growing demand is reflected in the fast-growing demand for Big Data technologies, which is expected to grow from 36.8 billion in 2018 to 104.3 billion in 2026 at a CAGR of 14 %, according to Fortune Business Insights.

Features of a Streaming Data Pipeline

The key elements for a good pipeline system are:

• Big Data compatibility
• Low latency
• Scalability
• Multiple options to handle different use cases
• Flexibility
• Cost-effectiveness

To make it cost-effective and meet the organizational needs, the Big Data pipeline system must include the following features:

• A Robust Big Data Framework with a high volume of storage Apache Hadoop.
• A publish-subscribe messaging system
• Machine learning algorithms to support predictive analysis support
• A flexible, backend storage for result data
• Reporting and visualization support
• Alert support to generate text or email alert

Tools for Data Pipeline in Real-Time

There are several tools available today for creating a data pipeline in real-time, collecting, analyzing and storing several millions of pieces of information for creating applications, analytics, and reporting.

We at Indium Software, with expertise and experience in Big Data technologies, recommend the following 5 tools to build real-time data pipeline:

• Amazon Web Services: We recommend this because of its ease of use at competitive rates. It offers several options such as Simple Storage Service (S3) and Elastic Block Store (EBS) to store large amounts of data which is supported by Amazon Relational Database Service for performance and optimization of transactional workloads. AWS also offers several tools for data mining and processing data. AWS Data Pipeline web enables the reliable processing and moving of data between different AWS compute and storage services. This is a highly available and scalable platform for your real-time data processing needs.

• Hadoop: Hadoop can be effectively used for distributed processing of huge data sets across different clusters of servers and machines parallelly. It uses MapReduce to process the data and Yarn to divide the tasks, responding to queries within hours if not seconds. It can handle Big Data volumes, performing complex transformations and computations in no time. Over time, other capabilities have been built on top of Hadoop to make it a truly effective software for real-time processing.

• Kafka: The open-source, distributed event streaming platform Apache Kafka enables the creation of high-performance data pipelines, data integration, streaming analytics, and mission-critical applications. Kafka Connect and Kafka Streams are two components that help in this. Businesses can combine messages, data and storage using Kafka whose other valuable components such as Confluent Schema Registry allows them to create the appropriate message structure. Simple SQL commands empower users to filter, transform and aggregate data streams for continuous stream processing using ksqlDB.

In addition to being used for batch applications and real-time, Kafka helps integrate with REST, files and JDBC, the non-event-streaming paradigm for communication. Kafka’s reliable messaging and processing with high availability makes it apt for small datasets such as bank transactions. The other two critical features, zero data loss and exactly once semantics, makes this ideal for real-time data pipeline creation along with streaming data manipulation capabilities. On-the-fly processing is made possible with Apache Kafka’s Streams API, a powerful, lightweight library.

• Spark: A popular open-source real-time data streaming tool promises performance and lowers latency. Spark Streaming enables the merging of streaming and historical data and supports Java, Python, and Scala programming languages. It also provides access to the various components of Apache Spark.

• Striim: Striim is fast becoming popular for streaming analytics and data transformations because of it being easy to implement and user-friendly. It has in-built messaging features to send alerts, ensures secured data migrations between, ease of data recovery in case of failures and agent-based approach for highly secured databases.

Indium has successfully deployed these technologies for its various data engineering projects for its customers across different industries, including banking, mobile app development and much more.

We have the experience and expertise to work on the latest data engineering technologies to provide the speed, accuracy and security that you desire for building data pipelines in real-time. Contact us for your streaming data engineering needs.

The post Top 5 Technologies to Build Real-Time Data Pipeline appeared first on Indium.