How to enable Unity Catalog for Azure Databricks?
Azure Databricks is an Azure managed service for provisioning
Databricks instances which is a platform that unifies data, analytics and AI.
Databricks users who have previously used older versions of Databricks may not
have migrated to Unity Catalog which is a centralized administrative module. This
article explains how to enable and work with Unity Catalog.
Databricks does not force us to migrate our data into
proprietary storage systems to use the platform. Instead, it allows us to integrate
the platform with external storage and deploys compute to process the data. We
control the integrations and manage permissions. Unity Catalog further extends
this relationship by managing permissions for accessing data using SQL syntax
from within Azure Databricks.
The primary purpose is integrated access control:
· Unity Catalog provides centralized access control, auditing,
lineage, and data discovery capabilities across Azure Databricks workspaces. It
offers a single place to administer data access policies that apply across all
workspaces and personas. It automatically captures user-level audit logs that
record access to your data. Unity Catalog also captures lineage data that
tracks how data assets are created and used across all languages and personas.
· An Azure managed identity can access external storage on behalf of
Unity Catalog users. Managed identities provide an identity for applications to
use when they connect to resources that support Azure Active Directory (Azure
AD) authentication.
The Unity Catalog comprises a hierarchy of Metastore at the
top level, followed by Catalog, then by Schema and Tables and views at the leaf
level. All items are referenced via a three-level namespace in the format
catalog.schema.table. Metastore is the top level container
for metadata. Other than the metastore, Unity Catalog comprises of user
management module.
The steps to follow to setup Unity
Catalog are:
1.
Configure a
storage container and Azure managed identity with read-write access to it.
2.
Create a metastore
3.
Attach workspaces
to the metastore
4.
Add users,
groups and service principals to the Azure Databricks account.
Many people struggle to follow
these steps because the navigation to get started is hidden behind their user
icon on the admin accounts portal under the menu item “Manage Account”. Once
they find this item, it is easy to follow the get started tutorial to create a
metastore and setup the unity catalog as directed.
The steps to follow for setting up an integration of a fresh new
instance with Azure data lake storage are:
1.
Create
an Azure Databricks instance in a vnet.
2.
Create
an ADB access connector resource for ADLS.
3.
Use
the access connector MI to access the Unity Catalog root storage account by
specifying the access connector id under Data->Metastore.
4.
Create
a storage credential in the Unity catalog for this Managed Identity
5.
Set
up your data lake storage account with storage firewall that allows only Optum
Ips
6.
Grant
access to this storage account by specifying to allow access from specific
resource type and the databricks instance.
7.
Setup
storage Credential with external location mapping and access control policies
for users and groups in the Unity Catalog.
Swaps of adjacent elements are able to be performed on nums.
A valid array meets the
following conditions:
·
The largest element (any
of the largest elements if there are multiple) is at the rightmost position in
the array.
·
The smallest element (any
of the smallest elements if there are multiple) is at the leftmost position in
the array.
Return the minimum swaps
required to make nums a valid array.
Example 1:
Input: nums =
[3,4,5,5,3,1]
Output: 6
Explanation: Perform the
following swaps:
- Swap 1: Swap
the 3rd and 4th elements, nums is then [3,4,5,3,5,1].
- Swap 2: Swap
the 4th and 5th elements, nums is then [3,4,5,3,1,5].
- Swap 3: Swap
the 3rd and 4th elements, nums is then [3,4,5,1,3,5].
- Swap 4: Swap
the 2nd and 3rd elements, nums is then [3,4,1,5,3,5].
- Swap 5: Swap
the 1st and 2nd elements, nums is then [3,1,4,5,3,5].
- Swap 6: Swap
the 0th and 1st elements, nums is then [1,3,4,5,3,5].
It can be shown
that 6 swaps is the minimum swaps required to make a valid array.
Example 2:
Input: nums = [9]
Output: 0
Explanation: The array is
already valid, so we return 0.
Constraints:
·
1 <= nums.length <= 105
·
1 <= nums[i] <= 105
Solution:
class Solution {
public int minimumSwaps(int[] nums) {
int min = Arrays.stream(nums).min().getAsInt();
int max = Arrays.stream(nums).max().getAsInt();
int count = 0;
while
(nums[0] !=
min && nums[nums.length-1] !=
max && count < 2 * nums.length)
{
var numsList = Arrays.stream(nums).boxed().collect(Collectors.toList());
var end = numsList.lastIndexOf(max);
for (int i =
end; i < nums.length-1; i++)
{
swap(nums, i, i+1);
count++;
}
numsList = Arrays.stream(nums).boxed().collect(Collectors.toList());
var start = numsList.indexOf(min);
for (int j =
start; j >= 1; j--) {
swap(nums, j, j-1);
count++;
}
}
return
count;
}
public void swap (int[] nums, int i, int j) {
int temp =
nums[j];
nums[j] = nums[i];
nums[i] = temp;
}
}
Input
nums =
[3,4,5,5,3,1]
Output
6
Expected
6
Input
nums =
[9]
Output
0
Expected
0