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5 Steps to Modernize Your Data Warehouse and Lake with Cloud-Native Data Management By Sampa Choudhuri

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As organizations modernize their data warehouses and lakes in the cloud, they need simple, high-performance solutions that work at a petabyte-scale. According to surveys of Informatica customers, 78% say they want data and analytics solutions that are easy, reliable, and simple to use. Modernizing your data warehouse and data lake doesn’t have to be complex… more
The post 5 Steps to Modernize Your Data Warehouse and Lake with Cloud-Native Data Management appeared first on The Informatica Blog – Perspectives for the Data Ready Enterprise.

As organizations modernize their data warehouses and lakes in the cloud, they need simple, high-performance solutions that work at a petabyte-scale. According to surveys of Informatica customers, 78% say they want data and analytics solutions that are easy, reliable, and simple to use. Modernizing your data warehouse and data lake doesn’t have to be complex if you follow some fundamental best practices and these 5 essential steps.

Challenges of data warehouse and lake modernization

Customers face many challenges in accelerating their cloud analytics and machine learning initiatives. In fact, according to McKinsey, about 70% of organizations seeking to scale digital business in the cloud will fail (i.e., have trouble getting started or getting completed on time and budget). And another industry survey reports that a majority of CIOs struggle to piece together data from multiple tools to assess the impact of IT investments on the business.

Cost and complexity can all increase as a result of common challenges:

  • Constantly growing data volumes
  • Lack of integration between point solutions
  • Failure to apply data quality and governance
  • Lack of end-to-end data pipeline maintenance
  • The difficulty in keeping up with rapidly changing technologies
  • Scarcity of resources

An additional challenge is that enterprises sometimes use different point solutions for each step involved in cloud analytics. For instance, to generate relevant insights as a result of cloud analytics and machine learning initiatives, you need to:

  • Ingest data at scale
  • Transform, cleanse and share the data
  • Govern and secure data
  • Extract insights

But if you’re using a different solution for each step, you end up with fragmented, incompatible systems, unable to scale (especially across multi-cloud), and expensive to maintain.

Setting Your Modern Data Warehouse and Data Lake Strategy

So, what is the right approach to take? The ideal strategy and approach will improve productivity, reduce manual work, and increase efficiencies through automation and scale. To achieve that, you need a comprehensive, multi-platform, multi-cloud data fabric with end-to-end data management capabilities that includes data integration, data quality, and metadata management so that you can make data useable for all users, realize rapid ROI, and accelerate time to value for your analytics initiatives.

Let’s break that into manageable steps and look at what’s involved. (You can also watch this short video for an overview.)

5 Steps to a Modern Data Warehouse and Data Lake

Let’s say you’re looking to quickly modernize your data warehouse to power business intelligence, data science, and analytics. What do you need to consider?

The diagram below shows a reference architecture for cloud data warehouses and lakes. The steps below correspond to each numbered zone (although I’ll leave off stream processing for real-time analytics for another time).

Reference architecture for cloud data warehouse | Informatica

First, just as a general principle, you will want to automate your entire data lifecycle. Intelligence and automation are critical for speed, scale, and agility, and faster time to market. Each of these steps benefits from metadata-driven AI capabilities that will allow you to radically increase speed and scale, as well as reuse your work across cloud platforms and processing engines.

Informatica has you covered with CLAIRE, an AI-powered engine that provides enterprise unified metadata intelligence to accelerate productivity across the entire Informatica Intelligent Data Platform. Using machine learning and other AI techniques, CLAIRE leverages the industry-leading active metadata capabilities to accelerate and automate core data management and governance processes.

Step 1: Discover and understand your data

The first step is to understand where data originates, its attributes, relationships, and lineage to have a complete picture of data and better govern the data. With Informatica’s data catalog and governance solutions, developers and citizen integrators can quickly identify the right data to migrate.

Using AI/ML and the CLAIRE AI engine’s automation capabilities, Informatica Enterprise Data Catalog can help organizations curate data for pipelines by exposing which datasets are available with relevant context. This reduces the time it typically takes for users to find and understand trusted relevant and available data.

Step 2: Ingest your data

Once you have identified the right data, you need to ingest the data into your cloud data lake. That typically involves an initial load from an on-premises data warehouse followed by an incremental load to capture “Change Data Capture” from the database.

With Informatica’s Cloud Mass Ingestion, developers can automate the loading of files, databases, and Change-Data-Capture records. The cloud-native solution allows you to quickly ingest any data at any latency with a simple, codeless, wizard-driven experience. 

Step 3: Ensure your data is trusted

Once you have ingested data into the data lake, you want to ensure your data is clean, trusted, and ready to consume. With Informatica’s cloud-native Data Integration and Data Quality solutions, developers and citizen integrators can use drag-and-drop capabilities in a simple visual interface to rapidly build, test, and deploy data pipelines.

Informatica Cloud Data Quality delivers the full range of data quality capabilities to ensure success—including data profiling, cleansing, deduplication, verification, and monitoring. Informatica Cloud Data Integration enables you to build high-performance end to end data pipelines quickly with a codeless interface. By abstracting away source and target systems the solution lets developers easily switch and move data workloads between using modern cloud data warehouses like Amazon Redshift, Azure Synapse Analytics, Snowflake or Google BigQuery or any cloud or on-premises systems simply by changing the connection.

Step 4: Create high-performance data processing pipelines for analytics

Once the data is in the cloud data warehouse, data consumers may want to further slice and dice datasets for data analytics. You can continue using the same visual designer to build your logic, while Informatica takes care of optimizing the execution using our multi-platform engine. 

Advanced pushdown optimization (or ELT) converts the mappings into native instructions and SQL queries and processes millions of records in just a few seconds, instantly giving you the data, you need to power your business insights. 

Suppose you are working with heavy data volumes for data science and machine learning projects. In that case, you can use Informatica’s elastic-scale Spark-based engine that is purpose-built to deal with big data and machine learning workloads. And you can use the same drag-and-drop visual designer to develop your mappings in a self-service manner.

With Informatica, you also have a choice of deployment models for your data pipelines. You can either control your infrastructure or opt for an advanced serverless deployment option with zero infrastructure management that aims to lower costs, simplify your operations, and increases efficiency of your IT resources.

Step 5: Provision data using DevOps practices

Today, agile experimentation is the new norm. Mature DevOps processes allow developers to focus on development while enabling them to ship bug-free code continuously through automated operation and monitoring capabilities that help ensure continuous integration and delivery, or CI/CD. Informatica’s cloud-native data platform lets you roll out DevOps practices to bring agility, productivity, and efficiency to your environment while lowering the cost of development. You can also get instant feedback by releasing more often, faster, and with fewer errors. Informatica cloud-native solutions provide out-of-the-box CI/CD capabilities that enable you to break down silos across development, operations, and security to deliver a consistent experience throughout the development lifecycle. 

Next Steps on Data Warehouse and Data Lake Modernization

Informatica offers industry-leading cloud-native data management solutions that help accelerate your path to cloud analytics and machine learning insights. Watch the video “How to put your data to work with a Unified, Intelligent Cloud-native Data Platform” for a quick overview. And be on the lookout for more videos and blogs going into detail about specific capabilities. You can also try the cloud data warehouse solution free for 30 days.

The post 5 Steps to Modernize Your Data Warehouse and Lake with Cloud-Native Data Management appeared first on The Informatica Blog – Perspectives for the Data Ready Enterprise.